Supreme Court extends mifepristone deadline

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Good morning. My co-workers and pals Isabella Cueto and Lev Facher have been talking about alcohol for years. As STAT’s reporters on chronic disease and addiction, respectively, it’s right at the intersection of their beats, yet rarely covered as a public health issue. I’m happy to share that all their talking turned to reporting, and now an incredible series. The first parts are up now. Scroll down or skip ahead to start reading

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Opinion: STAT+: Pharma and biotech leaders are destroying their own industry

In early 2025, biotech experienced a “DeepSeek moment” when biotech and pharma leaders alike realized how quickly China was gaining ground with innovation, speed of drug development, and share of licensing deals. In 2020, global pharmaceutical companies spent about $9 billion on licensed drug assets from China. In 2025, that number shot to more than $137 billion. The first two months of 2026 alone accounted for nearly $50 billion in deals. As a December 2025 report from the National Security Commission on Emerging Biotechnology put it, “in just three years, China’s biopharmaceutical industry rose from near irrelevance to dominance.”

China’s rise is happening with the blessing of U.S. pharmaceutical executives, who are allowing their own industry to be destroyed.

I am a co-chair of a working group at the Council on Foreign Relations investigating the U.S.’s generic pharmaceutical dependence on China. An estimated 60% of our generic medications have an active ingredient that originates in China; some estimates have this figure as high as 80-90%. (The exact percentage is unknown because the Food and Drug Administration doesn’t formally track this information, and because a significant percentage of our drugs are imported from India, which in turn imports chemical precursors from China.)

Continue to STAT+ to read the full story…

Integrating dual-process decision making and social dynamics: A formal modeling framework for addiction.

Psychological Review, Vol 133(4), Jul 2026, 864-891; doi:10.1037/rev0000584

Currently, formal models of addiction focus either on the complex individual decision-making processes involved in addiction or on the social dynamics of addiction. They do not integrate these two levels, which has been identified as a key shortcoming of current formal models of addiction. To address this, we propose a nonlinear dynamical modeling framework of addiction integrating both the individual level and social level of addictive behavior. The individual level of our modeling framework is a formalization of a dual-process theory, where one type of process increases the consumption of addictive goods, and another type of process limits consumption. For our formalization, we build on a well-studied model from ecology, originally used to model periodic outbreaks of the spruce budworm population. To this model, we add the process of incentive sensitization at the individual level and at the social level, we incorporate the critical processes of selection homophily and peer influence. We show that our integrated modeling framework can be used to explain key phenomena identified in addiction literature: a gradual transition to heavy use, sudden relapse and sudden quitting, relatively stable use states over time (i.e., abstinence moderate use, and heavy use), social contagion and sudden outbreaks, clustering of users, and social aid in recovery. In addition, we demonstrate how our modeling framework can be extended to include mutualistic, competitive, and more complex interactions between different addictive behaviors. Finally, we show how our framework can lead to new insights and predictions and suggest avenues for future research. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

STAT+: Trump pivots on kratom derivative 7-OH, floating approval for some forms

President Trump on Monday suggested the federal government could move to approve some forms of 7-OH, an opioid derived from the naturally occurring kratom plant.  

“We’re looking very seriously at natural 7-OH and getting that approved,” Trump said. 

It was not clear what Trump meant by “natural 7-OH.” Small amounts of the compound, shorthand for 7-hydroxymitragynine, occur naturally in kratom, which is increasingly used as a recreational drug and an unapproved pain treatment. While kratom is significantly less dangerous than potent synthetic opioids like fentanyl or prescription pain pills, it can still cause addiction and overdose. 

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What Is Traumatic Separation?

You may have a memory of being separated from a parent when you were a child, even just for a few minutes. Maybe you lost them in a crowd or wandered a little too far at the store and felt panicked and afraid.

A moment like this might be among your earliest memories because the feeling was so intense, says Caitlyn Downie, LCSW, the Director of Trauma and Resilience at the Child Mind Institute. That offers some insight into the fear of a child of any age who is separated from a parent or caregiver in a more serious way. The effects of this stress are so powerful they can actually change the way a child develops.

A toddler whose mother goes to prison. A kindergartener whose father is detained and deported. A teen who is placed in foster care. These are a few examples of what experts call traumatic separation, a clinical concept based on the importance of the parent-child bond and the profound effects that can result from breaking it.

What is traumatic separation?

Traumatic separation isn’t a clinical diagnosis, but research shows that it can be profoundly harmful to kids. What makes it traumatic (as opposed to routine partings, like when an adult regularly leaves their child to go to work) is the character of the separation: ones that are sudden, unexpected, or confusing, or those that come about through larger distressing events, like a natural disaster or war. It’s not defined by the time spent apart — both short and long-term separations can be harmful.

Some common examples of separation that can become traumatic include:

  • Parental deportation
  • Immigration (e.g., forced separation at the border)
  • Parental military deployment
  • Parental incarceration
  • Termination of parental rights

Separating from a parent or primary caregiver can be distressing to a child even when it’s deemed necessary for their safety, as in cases where the parent they have been separated from has abused them, says Kimberly Alexander, PsyD, a psychologist at the Child Mind Institute. “There’s still a natural attachment that occurs. And the separation disrupts that relationship, even if it’s for the support and care of the child.”

Why is traumatic separation harmful?

More than eight decades of research has shown the profound developmental importance of the parent-child bond. This is the guiding principle of attachment theory, which was pioneered by a British psychologist who studied children who were evacuated during the Blitz, the aerial bombardment of London in World War II.

Here’s what the research tells us about the harms of traumatic separation:

It can disrupt secure attachment

Think of secure attachment as a “fundamental sense of security and safety” that a child feels with a parent or caregiver, says Dylan Gee, PhD, a psychologist at Yale University who studies how early-life stress affects children’s development.

“Attachment is the lens through which children come to know what they can expect from the world around them,” she explains. “Is this going to be a safe place or a dangerous place? This is foundational to a child’s sense of their ability to navigate the world. Traumatic separation can shatter that sense of safety.”

It can affect neurobiological development

Children’s brains are especially plastic, says Dr. Gee, constantly learning to understand their environment and how to deal with stress. “Trauma that occurs in childhood can be even more consequential than trauma that occurs later in life,” she says, and experiencing these disruptions in childhood can affect the way your brain and body are primed to react to stress later on.

But heightened plasticity is a paradox, she adds. “It confers more vulnerability, but it also confers more potential for resilience — children have heightened potential for supportive intervention and for healing and recovery.”

What do the effects of traumatic separation look like?

There are acute and short-term effects that are common across kids of all ages:

Sleep problems: “It’s often one of the first things that we see: nightmares, trouble falling asleep, or a lot of crying as kids are trying to fall asleep,” Dr. Gee says.

Separation anxiety: This might look like distraction, withdrawal, or clinginess because of fear of being separated from their new caregivers, Dr. Alexander says.

But signs may take weeks or months to show up. Dr. Alexander advises caregivers to consider the child’s baseline — their typical patterns of eating, sleeping, or engaging with others. “If they’re having more trouble with sleep, they’re eating more, eating less, they’re withdrawing or expressing a lot of worried thoughts three or four months later — that’s something worth getting looked at by a clinician,” she says.

Signs of traumatic separation at different ages

“Sometimes people ask, ‘Well, when is separation the most harmful?’ It can be extremely harmful at any age,” Dr. Gee emphasizes. But there are specific signs at different developmental stages:

Infants

Babies may not be as consciously aware of being separated from a parent as older children, “but they’re fundamentally aware that their primary source of regulation and safety is missing,” Dr. Gee says. Because infants are so reliant on caregivers for nurturing and sustenance, the separation “can be experienced as a threat to their survival.” That might look like “crying a lot or becoming withdrawn,” she says. “And at any age we can see intense fear.”

Toddlers and young children (3–6)

Toddlers and young children might become extra clingy with new caregivers or show regressive behaviors like bedwetting or baby talk. Regressive behaviors happen when kids are overwhelmed by stress and can’t express themselves another way, Downie says. “It’s like your nervous system goes kind of haywire,” she explains, “so it uses the body to signal that something is wrong.”

Similarly, kids at this age might act out more, throwing more tantrums, or withdraw. They might develop selective mutism, a condition where kids are too anxious or distressed to speak, even when they want to, in certain situations or with certain people.

School-age children

School-age children might act out or experience separation anxiety. They may also struggle to understand the meaning of the separation, why it happened, or who is at fault for it. Thus, kids at this age are more prone to magical or distorted thinking and feelings of guilt, thinking or saying things like, “I’m the one that caused this” or “This is my fault.”

The weight of these distorted thoughts or other worries, Dr. Alexander says, might make it appear as though a child is struggling to concentrate or that they’re disengaged or distracted. They might withdraw in a group or be averse to stepping outside of their comfort zone.

Children who are school age or older can also experience emotional desensitization — a kind of emptiness of feeling — Downie says, which can look like spikes in irritability, a lack of empathy, not smiling or expressing positive emotions, or an inability to relate to others.

Preteens and teenagers

“I’ve seen teenagers have a lot of mistrust with systems and be very oppositional,” says Downie. “Like, ‘I don’t trust you. I don’t trust my teacher. I don’t trust this child services worker.’” It might make sense that, say, a teen in foster care would be wary of the foster care system. But Downie says it’s often a larger instinct for anger and mistrust, one that extends beyond any specific entity or person.

The teenage years are also when kids are forming their identity, and traumatic separation can fundamentally alter that process. For example, a teen with younger siblings may step into a parent role, taking on new worries and responsibilities. Conversely, teens may become more reckless in a caregiver’s absence, putting them at risk for substance abuse or incarceration.

How to help kids separated from a parent

Adults caring for a child who has been separated from a parent — family members, foster parents, teachers — “can play a profound role in supporting their mental health and resilience,” says Dr. Gee.

Validate feelings

One of the most important things caregivers can do is be present as a child reacts to their experiences, especially if and when scary feelings come up. But be careful not to lead kids or assume they feel a certain way. “You don’t want to make something more distressing to a child if it’s not presenting itself,” says Downie.

If a child expresses guilt, or says something like, “This is my fault,” there are still ways to validate the feeling without endorsing the statement, says Dr. Alexander. You might say something like: “I can understand why that thought comes to mind and how difficult it is to feel that way. When you’re ready, let’s think about other possibilities to this situation.”

Create consistency and stability

One of the hardest things about traumatic separation is the uncertainty — Where did they go? When will they come back? What is happening? Giving kids some sense of consistency and stability can help them feel safe despite the unknowns. So as much as possible, help them stick to any routines: going to school, seeing friends, doing activities they enjoy.

Dr. Alexander advises focusing on things you can control — for example, shielding kids from potentially worrying discussions in a family where a parent has been deported.

“There would likely be a lot of conversations in the home about the situation, maybe a lot of watching the news, maybe making a lot of phone calls to attorneys,” she explains. “So where are you having those conversations, and can you have them in an area or at a time of day where your kid isn’t overhearing the discussions out of context?”

For young kids, it might be as simple as asking them to play in their room. For teens, it might be better to have certain conversations when they are out of the house and invite them to participate directly in others.

Be honest but reassuring

Caregivers might not have all the answers — like knowing when a child’s parent is coming back — but they can create a sense of consistency and stability in how they respond to kids’ questions, too.

Avoid undue reassurance (“Everything is going to be fine”) or over-promising (“They’ll be back in two weeks”) by focusing on what kids can expect, says Dr. Gee. For example: “What I can tell you is that I’m here for you, and I’m going to be with you until he’s back,” or “You’re safe with me, and I’m going to stay with you through this really hard time.”

Model handling stress

Children are sensitive to tone, Dr. Alexander says. “So, if you’re having really big emotions that are out of context for a child, the child is looking at these emotions and trying to understand what’s happening. ‘Am I in danger in this specific moment?’”

She says it helps to have conversations about these moments, especially with younger kids. “Like, ‘I know you noticed mommy crying. We’re feeling really big feelings, and this is how we’re going to deal with those big feelings. I’m going to take a break. I’m going to get a sip of water. Whenever you’re having big feelings, I want you to let me know so that I can help you try doing the same things,’” Dr. Alexander says, explaining the importance of naming the emotion and then teaching kids that there are ways of dealing with it.

Long-term risks of traumatic separation

The effects of traumatic separation can persist even after a child and their caregiver are reunited. Traumatic separation, like other adverse childhood experiences, puts kids at risk for a host of long-term medical and mental health conditions, including depression, anxiety, attention issues, and post-traumatic stress disorder (PTSD).

But Downie notes that not everyone who experiences traumatic separation develops PTSD. “Just because someone’s experiencing trauma now doesn’t mean that it’s going to become a PTSD diagnosis,” she says. “A lot of the behaviors that we’re talking about are normal and expected. There’s an adjustment period when a separation happens.” But if symptoms persist or escalate over several months, a child may need more serious support.

Treatment for a trauma diagnosis

While not every child who experiences a separation may receive a trauma diagnosis or require treatment, cognitive behavioral therapy (CBT) — and the more specific trauma-focused cognitive behavioral therapy (TF-CBT) — is the “gold standard,” says Downie. TF-CBT is specifically for children experiencing trauma-related symptoms. An important component of TF-CBT is creating a trauma narrative, where kids create a story about what happened to help them process it. “But if you have a child who is not ready to process and integrate that trauma, you can’t force the pacing of the treatment,” she says.

In short, a good clinician will follow a child’s lead — even if that means just sitting in the same room with them to build trust. “People really need to feel like they’re being heard and that they can trust someone,” Downie says. Which is why a supportive caregiver or trusted adult can make a big difference.

“If people can take anything away from this, it’s that you want to make kids understand that that they’re not responsible for what’s happened and that people do care about them,” Downie says. “Kids are really resilient, and they can adapt in a good-enough environment. They don’t have to have everything to be successful.”

The post What Is Traumatic Separation? appeared first on Child Mind Institute.

<![CDATA[Psilocybin therapy shows fast, lasting relief for depression; clinicians discuss trial hurdles and emerging promise for PTSD and addiction in this podcast.]]>

Digital Therapeutic Content for Substance Use Disorder Treatment: Development and Evaluation Study

Background: Substance use disorders (SUDs) are a major public health concern, contributing to significant individual and societal costs. Despite this, the uptake of evidence-based pharmacologic and behavioral interventions remains limited. The digital delivery of SUD treatment has emerged as a potentially scalable way to reduce access barriers and increase treatment use. Existing digital therapeutic interventions are often created without clinician involvement, evidence-based materials, interdisciplinary input, or content review. The implementation of a structured and methodologically rigorous development process is needed across digital health interventions to help ensure patient-facing materials are validated, understandable, and actionable for the end user. Objective: This early report seeks to describe and evaluate an iterative, interdisciplinary, platform-agnostic process for adapting and refining existing print materials for digital therapeutic modules in SUD treatment. The a priori goal was to evaluate if a structured, human-centered approach would generate digital modules that were rated as understandable and actionable based on a validated assessment for written materials. Methods: Fourteen therapeutic modules were adapted from existing Mayo Clinic–written, patient-facing education materials originally developed by a board-certified addiction psychiatrist and a doctoral-level education specialist for clinical use. A team of 4 purposively recruited licensed alcohol and drug counselors with lived experience with a SUD, all in recovery, and a doctoral-level therapeutic specialist met weekly for one hour over a 6-month period to iteratively adapt this existing content for smartphone delivery (2‐3 hours per module). The process flow included selecting source material, restructuring content for viewing on a phone screen, simplifying language, improving organization and flow to promote understanding, and including specific actions users could take based on the content. The counselors then independently evaluated the modules using the Patient Education Materials Assessment Tool for printable materials (PEMAT-P). PEMAT-P scores for understandability and actionability were calculated as percentages, and descriptive statistics were used to summarize scores in aggregate and across modules. A target of >70% was set for each PEMAT-P domain, consistent with accepted benchmarking standards. Results: Mean understandability and actionability for all modules were 87.2% (SD 4.8%; range 81.4%‐96.9%) and 75.1% (SD 12.3%; range 57.1%‐95.0%), respectively, exceeding the recommended threshold. While all modules were adequately understandable, 35.7% (5/14) scored below the actionability threshold. Conclusions: This early report highlights the value of a human-centered, iterative process for adapting therapeutic materials for digital delivery in SUD treatment. Although the modules performed well overall on PEMAT-P benchmarks, actionability was less consistent than understandability, and aggregate scores masked weaknesses in several individual modules. This indicates that a standardized process does not guarantee actionable material across all content types. Involving current patients in this process may improve the end product by incorporating a perspective that was previously missed.

Oral Small-Molecule GLP-1s Linked to Deep Brain Activity and Reduced Cravings in Mice

Interest in glucagon-like peptide 1 receptor agonists (GLP-1s) continues to surge due to their effectiveness in reducing body weight and improving metabolic outcomes. This includes interest in small molecule oral GLP-1s which are more bioavailable and more easily manufactured than their injectable counterparts.

Now data from a new study in mice performed by scientists at the University of Virginia shows that this emerging class of weight-loss drugs suppress hedonic eating by modulating a reward circuit deep in the brain that is separate from previously described mechanisms that broadly affect appetite. The scientists believe that this pathway could be an avenue by which GLP-1s treat other dysfunctions in reward processing such as substance use disorders.

Details of the National Institutes of Health-funded study were published this week in a Nature paper titled “A brain reward circuit inhibited by next-generation weight-loss drugs in mice.” In it, the team reported that they investigated the small-molecule GLP-1s including Eli Lilly’s recently approved drug orforglipron, also known by the brand name Foundayo, as well as danuglipron, an oral GLP-1 that was being developed by Pfizer until the company decided to discontinue its development in 2025. 

Previous studies that explored the effects of larger peptide GLP-1s such as semaglutide in the brain have found that they suppress hunger-driven eating by engaging networks in the hypothalamus and hindbrain. What has been less clear is the mechanism by which small-molecule GLP-1s work. “As the accessibility of these medications continues to rise and patient uptake increases, it’s crucial that we understand the neural mechanisms underlying the effects we’re seeing,” said Lorenzo Leggio, MD, PhD, clinical director of NIH’s National Institute on Drug Abuse.

The current study gets scientists one step closer to that goal. According to the paper, the scientists first used gene editing to modify the GLP-1 receptors of mice to make them more humanlike. They then administered orforglipron or danuglipron to the mice, and identified brain regions where the drugs induced activity. The results showed that in addition to inducing activity in familiar pathways, the drugs also triggered the central amygdala, a region associated with desire that is deeper in the brain than scientists previously thought GLP-1s could directly reach. Further testing showed that once activated, the central amygdala reduced the release of dopamine into key hubs of the brain’s reward circuitry during hedonic feeding. 

“We’ve known that GLP-1 drugs suppress feeding behavior driven by energy demand,” said co-corresponding author Ali Guler, PhD, a professor of biology at the University of Virginia. “Now it seems oral small-molecule GLP-1s also dial back eating for pleasure by engaging a brain reward circuit.”

Given the effect of these drugs on eating for pleasure, future studies could explore whether small-molecule GLP-1s can also suppress cravings for other addictive substances. It is a question that the team hopes to explore in follow up studies focused specifically on substance use disorder. 

The post Oral Small-Molecule GLP-1s Linked to Deep Brain Activity and Reduced Cravings in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.

Mayo Clinic’s REDMOD AI Doubles Early Detection Sensitivity in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, with five-year survival rates below 15% and more than 85% of patients diagnosed only after the disease has metastasized. The absence of reliable early detection strategies is a primary barrier to improving outcomes. Conventional imaging, including standard abdominal CT scans, typically fails to identify PDAC during its preclinical, “visually occult” stage, when curative intervention is still possible.

To address this detection gap, a team of researchers at Mayo Clinic, led by radiologist and nuclear medicine specialist Ajit Goenka, MD, has developed and validated a radiomics-based artificial intelligence model called REDMOD (Radiomics-based Early Detection Model), which can detect subtle imaging signatures of PDAC before tumors are visible. By analyzing quantitative texture and structural features embedded within routine CT scans, REDMOD identifies early biological changes associated with carcinogenesis. In a multi-institutional validation study reflecting real-world clinical conditions, the model detected 73% of prediagnostic cancers at a median lead time of approximately 16 months—nearly doubling the sensitivity of radiologists manually reviewing the same scans. Notably, detection rates were even higher more than two years prior to diagnosis, pointing toward REDMOD’s potential for make much earlier interventions possible.

REDMOD’s automated pipeline integrates advanced radiomic feature engineering, including wavelet-based analysis, and an ensemble classification approach trained to handle the low-prevalence nature of early detection. Its longitudinal stability and consistent performance across diverse imaging systems could help spur its eventual clinical adoption.

Importantly, REDMOD is designed to operate on CT scans already acquired in routine care, particularly in high-risk populations such as individuals with new-onset diabetes. This raises the possibility of embedding AI-driven risk assessment directly into existing clinical workflows, enabling opportunistic screening without additional imaging burden. If validated prospectively, such as in the ongoing AI-PACED trial, REDMOD could shift the paradigm from late-stage diagnosis to proactive detection, potentially increasing the proportion of patients eligible for curative treatment and improving survival in this otherwise lethal disease.

Inside Precision Medicine recently interviewed Goenka to provide an in-depth view of the development of REDMOD, its detection capabilities, and its potential for providing early signals of the development of PDAC.

IPM: Can you walk through how REDMOD was developed, from the initial concept to a fully automated system, and what key technical breakthroughs enabled it to detect pancreatic cancer before tumors are visible?

Goenka: The origin of REDMOD traces back to a question we asked several years ago: if pancreatic cancer is almost always lethal because we find it too late, is there information already sitting in routine computed tomography (CT) scans that we are failing to extract? We published a proof-of-concept in Gastroenterology in 2022 showing that radiomic features from the pancreas could distinguish prediagnostic CTs from controls with high accuracy. But that first-generation model had real limitations. It relied on manual pancreas segmentation, which is labor-intensive and introduces variability. It was tested at a 1:1 case-to-control ratio, which does not reflect the rarity of pancreatic cancer in any realistic screening scenario. And it used a standard classifier without mechanisms to handle severe class imbalance.

REDMOD was built to systematically address each of those barriers. The first breakthrough was automating the front end of the pipeline. We developed and validated a fully automated volumetric pancreas segmentation model based on the three-dimensional (3D) nnU-Net architecture, published separately, which removes the human bottleneck entirely. That made the system scalable; you can run it on thousands of scans without a radiologist drawing a single contour.

The second breakthrough was in feature engineering. We extracted 968 quantitative radiomic features from each segmented pancreas, then applied multi-scale image filtering using wavelet transforms and Laplacian-of-Gaussian (LoG) filters. The wavelet decomposition breaks the image into eight directional sub-bands at different spatial frequencies, allowing the model to detect textural patterns at scales that the human eye cannot resolve. We then used the Minimum Redundancy Maximum Relevance (mRMR) algorithm to distill those 968 features down to 40 that carried the most predictive information. What emerged was striking: 90% of the selected features were filter-derived, meaning the signal lives in the texture of the tissue, not in anything visible on the standard grayscale image.

The third breakthrough was the ensemble classifier. Rather than relying on a single algorithm, REDMOD combines logistic regression, random forest, and extreme gradient boosting (XGBoost) through a soft-voting mechanism. Each algorithm processes the same 40 features; their probabilistic outputs are averaged to produce the final classification. This architecture achieved the highest sensitivity among all configurations we tested, 73%, which matters enormously in a disease where missing a case is effectively a death sentence. The entire system was trained using Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance inherent in early detection, and validated on an independent test set with a roughly 7:1 control-to-case ratio that approximates real-world prevalence in high-risk cohorts.

The fourth breakthrough, and one that distinguishes REDMOD from models that produce a simple binary output, is the pliability of the operating threshold. REDMOD generates a continuous probability score from zero to one. We used the Youden Index to define a statistically optimized default threshold (0.41), but this threshold can be adjusted to match different clinical objectives without retraining the model. In a non-invasive triage setting, the threshold can be lowered to maximize sensitivity, catching as many cancers as possible even at the cost of more false positives. When the clinical pathway moves toward invasive procedures such as biopsy, the threshold can be raised to prioritize specificity and precision, reducing the risk of subjecting healthy patients to unnecessary procedures. This tunability means that a single trained model can serve multiple roles across the clinical cascade, from initial risk stratification through confirmatory workup.

IPM: The model relies heavily on radiomic features, particularly wavelet-filtered textures. What do these features capture biologically, and why are they better suited to detecting early pancreatic cancer than conventional imaging markers?

Goenka: Conventional imaging markers for pancreatic cancer, such as a visible mass, ductal dilation, or vascular involvement, are late manifestations. By the time you see them, the disease has typically been present for years. What we needed was a way to detect the biological processes that precede mass formation.

Radiomic texture features quantify the spatial relationships between voxels, which are the three-dimensional equivalent of pixels. They measure how intensity values co-occur, how they cluster, and how uniform or heterogeneous the tissue appears at different scales. Specifically, features derived from the Gray-Level Co-occurrence Matrix (GLCM) measure local patterns of intensity variation; Gray-Level Size Zone Matrix (GLSZM) features capture the distribution of connected regions of similar intensity; and Gray-Level Dependence Matrix (GLDM) features quantify how dependent each voxel’s value is on its neighbors. These are mathematical descriptions of tissue microarchitecture.

The wavelet filtering is what makes this work in the prediagnostic setting. A wavelet transform decomposes the image into sub-bands that isolate different spatial frequencies and directions. This allows the model to detect textural disruptions across multiple scales: fine-grained changes that might reflect early stromal remodeling or desmoplastic reaction, and coarser patterns that could correspond to alterations in parenchymal organization. When we performed ablation studies, models built from filtered features alone matched the full REDMOD performance (area under the receiver operating characteristic curve [AUC] of 0.82), while models restricted to unfiltered features dropped to 0.74. That 8-point difference was statistically significant and tells us that the prediagnostic signal is fundamentally a multi-scale textural phenomenon.

Biologically, this aligns with what we know about early pancreatic carcinogenesis. Before a mass forms, the tumor microenvironment undergoes extracellular matrix remodeling, fibrotic changes, and shifts in cellular density that alter tissue texture at microscopic scales. These changes are invisible to a radiologist reading the scan on a monitor, but they leave a quantitative fingerprint in the image data. That fingerprint is what REDMOD reads.

IPM: How did you assemble the training dataset, and why was it important to simulate a low-prevalence, real-world screening environment?

Goenka: Assembling the dataset was one of the most labor-intensive aspects of this work, because prediagnostic CT scans are inherently rare. These are scans obtained for unrelated clinical reasons in patients who were later diagnosed with pancreatic cancer, but at the time of the scan, the pancreas appeared entirely normal on radiology review. We identified 219 such patients across the Mayo Clinic enterprise, with scans obtained three to 36 months before histopathologic diagnosis. Each was verified by expert radiologists to confirm the absence of any discernible pancreatic abnormality.

The control cohort comprised 1,243 patients whose CT scans showed a normal pancreas and who remained cancer-free for at least three years of follow-up. That three-year washout period was essential; without it, you risk contaminating the control group with patients who had undetected cancer at the time of their scan.

We then split the full cohort into 969 training cases and 493 test cases, with the test set held completely independent. The resulting control-to-case ratio of approximately 7:1 was a deliberate design choice. Most artificial intelligence (AI) studies in this space have used balanced 1:1 ratios, which inflate performance metrics and do not reflect the reality of early detection. In any high-risk cohort you would screen clinically, for example patients with new-onset diabetes and elevated Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) scores, pancreatic cancer prevalence is roughly 3-4%. If you train and test your model at 1:1, you get numbers that look strong in a paper but collapse when deployed in a real population. We wanted REDMOD’s reported performance to approximate what a clinician would actually experience.

IPM: You validated the model across multiple institutions, imaging systems, and external datasets. What were the biggest challenges in ensuring consistent performance across such heterogeneous data?

Goenka: The central challenge is that CT scans are not standardized. Different hospitals use different scanners from different manufacturers, different acquisition protocols, different reconstruction algorithms, and different contrast timing. All of these affect the pixel-level values that radiomic features depend on. A model that works well on data from one scanner can fail on data from another.

We addressed this at multiple levels. First, our prediagnostic cohort was inherently heterogeneous. 71% of the prediagnostic CTs in the test set were acquired at external institutions, not at Mayo Clinic. These scans came from a range of scanners (Siemens, GE, Toshiba, Philips) and clinical settings. Second, we validated specificity on two independent external cohorts: a multi-institutional dataset drawn from the Mayo Clinic enterprise across multiple campuses, and the National Institutes of Health Pancreas CT (NIH-PCT) dataset, which is a publicly available benchmark that uses entirely different acquisition parameters. REDMOD achieved 87.5% specificity on the NIH-PCT dataset, data the model had never encountered and that was acquired under conditions completely outside our control.

Third, we performed a longitudinal test-retest analysis. For patients with serial CT scans, we assessed whether REDMOD produced consistent predictions across time points. The concordance rate was 90-92%, meaning the model’s output was stable despite natural variations in patient hydration, contrast timing, and physiologic state between scans. That kind of temporal stability is essential for any tool used in a surveillance context, where you need to trust that a change in the model’s output reflects a real biological change, not scanner noise.

IPM: How do you see REDMOD being integrated into existing clinical workflows, for example in evaluating incidental CT scans or screening high-risk groups like patients with new-onset diabetes?

Goenka: The population where this has the most immediate clinical relevance is individuals with glycemically-defined new-onset diabetes (gNOD) and an ENDPAC score of three or higher. This is a well-characterized high-risk group with a 3-4% short-term risk of developing pancreatic cancer, roughly 20 times the general population rate. Many of these patients already receive CT scans for other clinical indications. The question is not whether to scan them; the question is whether we are extracting all the information those scans already contain. We were not. REDMOD changes that.

The workflow we envision is not a population-wide screening program. It is a targeted, risk-stratified approach. An electronic medical record (EMR)-based algorithm identifies patients who meet gNOD and ENDPAC criteria. When those patients undergo a CT scan, either for clinical reasons or as part of a structured surveillance protocol, REDMOD runs in the background, analyzes the pancreas automatically, and generates a risk score. If the score exceeds a defined threshold, it triggers a clinical pathway: the referring physician is notified, and the patient enters a structured workup that could include enhanced imaging, molecular imaging with fibroblast activation protein (FAP)-targeted positron emission tomography (PET) radiotracers, or closer follow-up.

REDMOD does not replace the radiologist. The radiologist reads the scan according to standard practice and generates their clinical report independently. REDMOD operates as a parallel, complementary layer, a second opinion from a system that reads data the human eye cannot access. The physician integrates both sources of information to make clinical decisions.

This is precisely the model we are testing in the AI-PACED (Artificial Intelligence for Pancreatic Cancer Early Detection) prospective clinical trial at Mayo Clinic. In this trial, all CT scans are interpreted by non-study radiologists who are blinded to the study objectives, and their reports enter the patient’s medical record as part of routine clinical care. Independently, the AI analysis is performed on de-identified data on secure research servers. A strict firewall separates the two: AI-generated outputs are not integrated into the EMR, are not communicated to the clinical team, and are not used to guide diagnosis or treatment. This dual-layered design ensures that participants receive the benefit of structured clinical surveillance while allowing a blinded, independent evaluation of the AI’s performance.

IPM: With the AI-PACED prospective trial underway, what are the key questions you still need to answer about clinical utility, false positives, and patient outcomes before this technology can become part of standard care?

Goenka: There are several questions that retrospective data alone cannot answer, and AI-PACED is designed to address them.

The first is lead-time advantage. We know REDMOD detects prediagnostic signal at a median of 475 days before clinical diagnosis in retrospective data. The question is whether that lead time translates into an actual shift in diagnostic timing in a prospective setting, that is, whether patients in a structured AI-augmented surveillance protocol receive their diagnosis earlier, and at a more resectable stage, compared to patients receiving symptom-driven standard care. The trial’s primary endpoint is the time-to-diagnosis from gNOD onset, compared between the interventional and observational cohorts using Kaplan-Meier survival analysis and Cox proportional hazards modeling.

The second is false positives. In the retrospective validation, REDMOD had an 81% specificity, which means approximately 19% of healthy patients received a positive flag. In a low-prevalence screening population, even a modest false positive rate generates a meaningful number of patients who undergo additional workup for a cancer they do not have. AI-PACED will quantify the downstream diagnostic burden, including additional imaging studies, biopsies, and the psychological impact, so we can make an honest assessment of the risk-benefit tradeoff. It is worth noting that REDMOD’s precision of 36.2% at its default operating point already exceeds the 3% precision threshold recommended by the United Kingdom’s National Institute for Health and Care Excellence (NICE) at the first step of cancer referral, and established screening programs for lung and breast cancer accept similar tradeoffs at their initial triage steps.

The third is adherence. This is a surveillance protocol in asymptomatic people. They feel fine. Asking them to return for serial CT scans and blood draws over 12 months requires trust, and that trust has to be earned through transparency about what we know and what we do not know. AI-PACED will measure recruitment yield from EMR-identified high-risk individuals, retention rates across the imaging and biobanking protocol, and the practical challenges of integrating AI into existing radiology workflows without disrupting standard care.

The fourth, and perhaps most important for the long term, is whether earlier detection actually changes outcomes. Stage shift, moving a patient from stage IV to stage I or II, is necessary but not sufficient. We need evidence that patients diagnosed through AI-augmented surveillance live longer, have access to curative surgical resection, and experience better quality of life. That is the bar this technology must clear, and it is the bar we intend to hold ourselves to.

The ongoing phase of AI-PACED is a feasibility study. It is designed to generate the operational, logistical, and preliminary clinical data needed to justify and design a fully powered, multi-institutional trial. In addition, we are running in silico clinical trials and cost-effectiveness analyses. We are building the evidence base one layer at a time, because the stakes, for patients and for the credibility of AI in clinical medicine, are too high to cut corners.

 

The post Mayo Clinic’s REDMOD AI Doubles Early Detection Sensitivity in Pancreatic Cancer appeared first on Inside Precision Medicine.

“Failure to Launch” Syndrome: How to Stop Enabling Your Grown Child

When Zeke was in high school, he struggled with anxiety and substance use problems, and he left college after the first semester. Now 25, he is living at home, and his mom Carol is frustrated. While she’s pushed him to go back to school or work, he has only held one part-time job at a local smoothie shop and quit after a few months, embarrassed that high school classmates would see him working there. Another attempt at trade school to become an electrician also didn’t take — it didn’t feel like the right fit. Now he rarely leaves the house, stays up all night playing video games or scrolling online, and sleeps most of the day.

Failure to launch syndrome, highly dependent adult children, boomerang kids — there’s no standard term or definition, but if you’re a parent in this situation you recognize it. You are worried and frustrated about your adult child’s difficulty in leaving the nest, and you don’t know what to do because everything you’ve tried so far hasn’t worked. 

“These aren’t kids who come back home because they finished school, and the first job they get doesn’t pay enough for them to afford rent on an apartment,” says Theresa Welles, the Shapiro Family Director of the Bubrick Center for Pediatric OCD at the Child Mind Institute. “We’re talking about young adults who functionally have hit a wall, so to speak. They’re caught in a loop of dependency.”

What is failure to launch syndrome?

It’s not uncommon for adult children to live with their parents: According to Pew Research Center, 18 percent of adults ages 25 to 34 lived in their parents’ home in 2023, with young men more likely than young women to do so (20 percent vs. 15 percent). Young adults might leave home for a period of time and then move back in with their parents because they can’t find a job. Or for religious or cultural reasons, some adult children expect to live in the family home until they get married. Living at home is not the main criterion for determining a “failure to launch.”

While there is no official clinical definition, researchers who study this group of young adults generally categorize someone as a highly dependent adult child if they are:

  • Not in school, working, or actively looking for work (though physically capable of doing so)
  • Financially dependent on their parents for housing and other necessities
  • Emotionally reliant on parents (i.e., needing constant reassurance that they are okay)  

They usually have very limited social interactions other than online. Often, they have mental health challenges such as anxiety, depression, or OCD, which is a contributing factor, Dr. Welles says.

“They’re at the developmental stage of early adulthood, they’re figuring out who they are,” Dr. Welles says. “The fancy term in psychology is ‘individuation,’ but it’s essentially who you are, both as part of your family and separate from your family.” Highly dependent adult children haven’t made much progress in this stage for several years. Many of them want to change their life path and become more independent, but they struggle with anxiety or fear of failure and don’t follow through on the necessary steps. “Reliance on parents reduces opportunities to build autonomy, which in turn maintains that reliance,” she says. So, they remain stuck.  

Dependent behaviors and parental accommodations

Young adults who are highly dependent often fall into certain patterns of behavior. They don’t do their own laundry, cook, clean, or help out around the house. They rarely leave the home and often shut themselves in their bedroom or live in the basement, avoiding talking to others in person. As a result, they rely on their parents to act as an intermediary with the outside world, such as making doctor’s appointments. They might blame their parents for their difficulties in life.

While parents may not like the situation, they struggle to get their adult child to change. So instead, they accommodate them — especially when they are concerned about their child’s mental health challenges.

“In the world of neurodiversity, accommodations are a good thing — we want accommodations for testing and sensory environments,” says Natalia Aíza, LPC, the author of the forthcoming Anxious to Launch: Parenting Strategies to Help Your Adult Child Move On. “But in the anxious-to-launch world, accommodations are actually interfering with your child becoming independent.”

Aíza gives some examples of unhelpful family accommodations: You make sure there’s food in the fridge, don’t ask them to contribute to paying bills, and may give them spending money. When they get angry or upset, you accept the behavior and feel guilty, thinking you are to blame for the situation. If they are anxious when you aren’t nearby, you don’t travel because it causes them stress. Instead of expecting them to take steps to find a therapist, you do the legwork.

“The number one behavior of the highly dependent adult child is avoidance. I cannot emphasize this enough,” Aíza says. “If your child has a full-on virtual life, that’s their social outlet. They are avoiding real-life challenges. They are avoiding working at jobs that are unpleasant. They are probably avoiding adulting tasks that should fall on them at this point. So, we swoop in and take care of those tasks for them.”

A modern version of an old problem

While adult children have lived with their parents in past generations, researchers argue that phenomenon of highly dependent adult children is on the rise, and young people today seem particularly susceptible. Adolescence is more prolonged now in many cultures, and there’s an emphasis on finding a fulfilling career, not just a job that pays the bills.

Technology contributes to the problem. Playing video games, watching videos, scrolling through social media — “these activities don’t help matters because they can do things that feel like they’re accomplishing something,” Dr. Welles says.  

How to stop enabling your grown child

In Dr. Welles’s practice, she has worked with families where she initially treated the teen for anxiety or OCD, then involved the parents more deeply when the young adult had trouble launching. In one case, the son was in the habit of playing video games late at night and would sleep through class the next day. He had anxiety and depression, and his parents didn’t want to take away video games because it was the one thing he enjoyed doing. But they started turning off the Wi-Fi in the house at a certain time at night.

“It sounds so extreme, like he’s being punished,” Dr. Welles says. “But it’s about saying to him, ‘We’re going to pull back on ways we’ve accommodated that may have unintentionally made your anxiety worse.’” It was important that the parents validated his feelings, saying things like, “You feel like you’re in danger, as if you’re standing in front of a bear, and that’s really hard. But that’s the anxiety lying to you, and it won’t go away if we keep accommodating things that allow you to avoid what you need to do in order to overcome this anxiety.”

And tactics like these made a difference over time. The son is now attending college part-time and working as a server at restaurant. He has a girlfriend and has plans to save enough to move into an apartment with a friend.

Setting boundaries with your adult child

If the adult child doesn’t seem motivated to find a job, Aíza has recommended that parents take them off the family cellphone plan, giving them warning that this will happen by the next month’s bill. “This is not necessarily the most strategic financial choice” because it’s often much cheaper per person on a family plan, she acknowledges. “But it is a perfect first accommodation to remove because it is telling your adult child, ‘This is something you can handle. You can be responsible for it financially and logistically. It is something that I control, and I want to stop controlling parts of your life.’” And it’s often the motivation they need to find a job — something that can earn them $100 for the monthly cell phone bill is small enough that it feels doable.

When families take steps like these, the adult child will likely get angry or upset. “That’s hard. But think about when your kids were toddlers, and they wanted to touch a hot stove,” Dr. Welles says. “They were mad when you said, ‘No, you can’t touch that stove,’ but that didn’t mean you let them do it.”

“The good news is, generally speaking, even if there’s unhappiness in the beginning,” she continues, “pretty quickly, once they start to feel better and are doing the things that they actually care about, it can really help.”

Supporting without enabling adult children

Highly dependent adult children might accuse parents of not being supportive when they pull back on accommodations. Dr. Welles suggests communicating that you hear them and validate their feelings: “You can say things like, ‘Hey, I know this is tough or ‘I know that this makes you really nervous.’ But you combine it with the confidence that they can do it, like ‘I also know you can do it, as hard as it is.’”

Sometimes, you might think you are being supportive when you are actually enabling — like filling out a job application on behalf of the child. “Even if it works and they get an interview, you’re accommodating their anxiety,” Dr. Welles says. “But also, there’s going to be a point when you can’t do something for the child — the interview or the job itself — so the earlier that you can pull back the better.”

If your adult child has both ADHD and anxiety, you can support their executive functioning skills without accommodating the anxiety. “Maybe you sit down with them on Mondays and look at their schedule to help them determine if there’s a way you can help them organize, as opposed to you stepping in and letting them avoid things they need to do because they’re anxious about it,” Dr. Welles says.

Aíza encourages giving the adult child the minimum amount of help needed, to avoid creating another form of dependency. “It’s about noticing, ‘Am I working harder at this than they are?’” she says. “A lot of times the answer is ‘yes,’ and that’s a signal to back off and put more expectations on the child.”

Treatment for highly dependent adult children

While there is no standard treatment for highly dependent adult children, early evidence has shown a form of therapy called SPACE-FTL (Supportive Parenting for Anxious Childhood Emotions – Failure to Launch) to be promising. A variation on an effective treatment for anxiety and OCD, SPACE-FTL involves only the parents, since the adult child is often resistant to seeking help. The program helps parents reduce accommodations step by step and engage extended family and friends to help de-escalate conflict. 

One tactic is to make a plan to deliver a change in accommodation in writing — for instance, explaining that you will stop paying the cellphone bill at the end of the month and why. Doing it in writing (on paper or in a text) makes the message clear and helps you remain calm and non-reactive. If you are expecting an angry or violent response, they can ask a grandparent, uncle, or family friend be in the house when you deliver the letter, since that might make the response less extreme. The relative or friend may even spend the night if the adult child is more likely to cool off when others are present.

Asking for others’ help also helps you stop blaming yourself for the situation. “A lot of parents of highly dependent adults feel shame, but this is not something happening to only one family,” Aíza says. “We need to build on our social supports and get other people on our team so that we don’t feel so isolated in this process. Your adult child may be resisting change, but you don’t have to. It might sound cruel, but our central mandate as parents is making sure our child is okay after we’re gone. We brought them on earth to survive us — that is the design.”

Frequently Asked Questions

What is “failure to launch syndrome”?

“Failure to launch” isn’t a formal diagnosis but describes young adults who are stuck in a pattern of dependence. They’re typically not working or in school, rely on parents financially and emotionally, and struggle to move forward with adult responsibilities.

How can I motivate my adult child to become independent?

Change often starts with parents gradually pulling back on accommodations while staying supportive and calm. Set clear expectations, validate their feelings, and shift responsibility back to them in manageable steps so they can build confidence and autonomy.

The post “Failure to Launch” Syndrome: How to Stop Enabling Your Grown Child appeared first on Child Mind Institute.