Background: The global demand for assistive devices, such as prosthetics and orthotics, is increasing. A shortage of trained professionals contributes to suboptimal care. To improve clinical workflows, the Life Lounge Clinical Workflow (LLCW) has been developed. Understanding user acceptance is essential for ensuring its successful implementation. Objective: This study explored prosthetists and orthotists professionals’ perceptions and acceptance of LLCW, as well as the perceived benefits and challenges associated with its use. Methods: A postdemonstration mixed methods study was conducted using the unified theory of acceptance and use of technology framework, combining Likert-scale summaries with thematic analysis of open-text responses. The study included 18 prosthetists and orthotists professionals working at orthotic and prosthetic clinics across Sweden. After an interactive session about LLCW, feedback was collected via questionnaires. Thematic analysis was used to analyze the data. Results: Participants rated several acceptance-related constructs positively. Performance expectancy and facilitating conditions emerged as the most favorably discussed areas in the qualitative responses. Descriptive ratings showed high mean scores for motivation to use (4.61), management encouragement (4.56), ease of use (4.11), and willingness to use voluntarily (4.11). However, colleagues’ perceptions had a lower mean rating (2.72). Participants highlighted centralized data access, reduced administrative tasks, and improved clinical preparation as key benefits. At the same time, concerns were raised regarding data accuracy, questionnaire length, and the need for structured training before implementation. Conclusions: Participants reported generally positive experiences with LLCW, particularly regarding usability and performance. However, successful implementation requires integration into existing clinical workflows and attention to training and patient engagement. Addressing these elements can support broader adoption and contribute to digital transformation in prosthetics and orthotics care.
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Understanding Online Health Information Consumption Through Web Analytics of the Italian Society of Pharmacology Magazine: 3-Year Descriptive Analysis
<strong>Background:</strong> The COVID-19 pandemic underscored that access to reliable and expert-driven scientific information is not only essential but also lifesaving. Since 2020, the Italian Society of Pharmacology has been publishing <i>SIF Magazine</i>, an online magazine dedicated to citizens. This journal was created to make pharmacology accessible to the public, highlighting its impact on health and quality of life while clarifying the truths, theories, and misconceptions surrounding drugs and their use. <strong>Objective:</strong> This work analyzed web interaction data from <i>SIF Magazine</i> to understand how the public reaches and engages with an online scientific journal and gather practical insights for improving digital scientific communication. <strong>Methods:</strong> The data analyzed in this study were obtained from the web analytics of the <i>SIF Magazine</i> website. The analysis covers 3 years (2022-2024). By studying patterns of access, navigation, and engagement, the analysis clarified which types of scientific content connect most with users, how people find and choose trustworthy sources, and what they do after reaching them. <strong>Results:</strong> Average monthly site visits increased from 120,024 in the partial period examined in 2022 to 128,059 in 2023 and 200,379 in 2024, paralleled by higher monthly views (155,785 in 2022, 165,438 in 2023, and 254,297 in 2024). The engagement rate declined modestly (36% in late 2022, 35% in 2023, and 29% in 2024), consistent with scale-related dilution from an expanding top-of-funnel audience. Category-level analyses of top-performing articles indicated disproportionate interest in renal, urogenital, and sexual disorders followed by inflammation and pain and gastrointestinal diseases. Seasonal analyses showed recurrent peaks for season-linked topics (eg, motion sickness, photosensitivity reactions, and influenza vaccination) during expected periods. <strong>Conclusions:</strong> Together, these findings underscore the importance of data-driven content planning and continuous performance monitoring to sustain the effectiveness of digital scientific communication platforms.
Anxiety and Depression Associated With the Dependent Use of Generative AI in Medical Students: Cross-Sectional Study
Background: The growing integration of artificial intelligence (AI) in higher education has transformed learning processes but also raised concerns about potential mental health risks. Medical students represent a particularly vulnerable group due to high academic stress and increasing reliance on generative AI tools for study and decision-making tasks. Despite this, the relationship between AI dependence and psychological distress remains underexplored in Latin American contexts. Objective: This study aimed to evaluate the association between generative AI dependence and levels of stress, anxiety, and depression among medical students. Methods: A cross-sectional study was conducted with 187 human medicine students from a Peruvian university during the first academic semester of 2025. The Dependence on Artificial Intelligence Scale and the Depression, Anxiety, and Stress Scale–21 were applied. Negative binomial regression models, both crude and adjusted for sex, age, income, and year of study, were used to assess associations, reporting rate ratios (RRs) and 95% CIs. Results: Participants had a median age of 22 (IQR 19‐24) years, and 58.8% (110/187) were female. The median Dependence on Artificial Intelligence Scale score was 10 (IQR 7‐14). Generative AI dependence showed significant correlations with anxiety (ρ=0.336, 95% CI 0.22‐0.44) and depression (ρ=0.316, 95% CI 0.20‐0.43) and a smaller correlation with stress (ρ=0.277, 95% CI 0.16‐0.39). In the adjusted regression models, each 1-point increase in generative AI dependence was associated with a 5% higher expected anxiety score (RR 1.05, 95% CI 1.01‐1.09; =.01) and a 4% higher depression score (RR 1.04, 95% CI 1.01‐1.08; =.03), whereas the association with stress was positive but nonsignificant (RR 1.03, 95% CI 1.00‐1.07; =.08). Fifth-year students had significantly greater anxiety levels than their sixth-year peers (RR 1.82, 95% CI 1.09‐3.01; =.02). No significant effects were observed for sex, age, or income. Conclusions: This study empirically examined generative AI dependence as a distinct behavioral construct and its association with mental health symptoms in medical students. Unlike prior research, this study evaluated psychological dependence on generative AI and modeled its relationship with anxiety and depression using appropriate count-based regression techniques. By providing early evidence from a Latin American context, it contributes to the emerging field of digital mental health and medical education research. These findings underscore the need for universities to promote balanced and responsible AI use, integrate digital literacy with mental health support strategies, and develop preventive policies that mitigate potential maladaptive reliance on generative AI tools.
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Effect of Wearable Activity Tracker Social Behaviors on Physical Activity and Exercise Self-Efficacy: Real-World Pilot Study
Background: Wearable activity trackers are useful tools to track and monitor physical activity (PA), especially considering their use in free-living environments. Users often see moderate improvements in step count, but consistent increases at various intensities of PA are inconclusive. While wearable research is growing, no known studies specifically examine the relationship between how the use of self-selected social features on wearables affects PA and exercise self-efficacy. Objective: This study aims to compare weekly PA, approximating moderate-to-vigorous intensity, of adults from the New York City metropolitan area assigned to either use or not use social engagement PA features on their device. Exercise self-efficacy was also measured. Additionally, a preliminary examination into the use of 3 different social features was conducted to inform where controlled parameters on feature use may be needed in future work. Methods: The researchers conducted a real-world pilot study by recruiting wearable users aged 18 years and older in the New York City area to wear their devices in free-living environments. After consent, participants were randomized into 1 of 2 conditions: the condition that involved use of the social engagement PA features or the condition that did not for 8 weeks. Participants submitted objective data from their device and completed a self-efficacy measure at baseline, week 4, and week 8. Those in the intervention group also answered questions about which social feature they used the most throughout the study. Results: Data from 123 participants were analyzed using mixed methods analysis. Principal findings included no difference between wearable social feature users and nonusers in weekly PA (=.55) or exercise self-efficacy (=.47). There was an overall effect of time across the repeated measures on PA (=.006) with an average increase of 72 (SD 3) minutes. Secondary findings highlight the need to control for the use of only a single social feature to identify more concrete effects. An effect of time was found across the repeated measures (=.01) in the intervention group, showing an increase of 49 to 126 minutes of PA, depending on the feature used most. The mixed methods analysis also found that exercise self-efficacy did not significantly change based on which social feature was used most (=.24). Conclusions: Consistent with other literature, this pilot study demonstrates that using wearables can lead to increases in PA and that sharing one’s PA data with others may amplify the effect. However, the novelty of this study is that although carefully implied, specific social features on a wearable may have a greater effect than others. This study identified the need for further investigation into which features may be more effective. With the increased prevalence of device ownership, knowing if certain social features lead to greater increases in PA may help those encouraging PA behavior change.
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Outdoor Secondhand Smoke Exposure Around a Public Smoking Area: Formative Field Study Using Passive Wi-Fi Packet Sensing
Background: Outdoor secondhand smoke (SHS) remains a public health concern, particularly around designated outdoor smoking areas where nonsmokers may pass through or remain nearby. Although prior studies have quantified outdoor SHS concentrations, fewer have examined how many people may be present within a plausible exposure setting. Estimating the exposure-opportunity level requires methods that are feasible, scalable, and minimally intrusive. Objective: This study aimed to evaluate the feasibility of using passive Wi-Fi packet sensing, calibrated with brief on-site observation, to estimate the number of smokers and passersby within a plausible SHS exposure range at a public outdoor smoking area in Japan. Methods: We conducted a formative field study at a designated outdoor smoking area at the Asia Pacific Trade Center in Osaka, Japan. A passive Wi-Fi packet sensor collected timestamps, anonymized device identifiers, organizationally unique identifiers, and received signal strength indicator (RSSI) values from October 13 to 29, 2023. The main analysis focused on October 28, 2023, a high-footfall event day selected for direct calibration. Episodes were classified using empirically derived RSSI thresholds, and class-specific calibration ratios were applied to estimate day-level counts. Results: Of 128,313 anonymized detections recorded on October 28, 90.3% (115,950/128,313) occurred during business hours. Among these, 8.6% (n=11,068) identifiers were detected more than once. Dwell time could be calculated for 1.4% (n=1817) of the identifiers, and 0.5% (n=659) eligible presence episodes remained after preprocessing. During a 30-minute validation window, smokers and passersby were counted manually within a 25-m radius. During the validation window, 6230 signal records formed 104 stays, with a mean stay duration of 9.89 (SD 7.89) minutes. During the validation window, direct observation recorded 14 smokers and 207 passersby within the 25-m radius. Applying the rule-based classification and calibration ratios to business hours data yielded estimated day totals of 262 smokers and 3907 passersby within the plausible SHS exposure range. Estimated smoker counts showed 2 peaks, around noon and 4 PM, whereas passerby volume peaked around midday. In an exploratory analysis, a random forest model using stay duration, mean RSSI, and RSSI variability achieved an accuracy of 0.95, sensitivity of 0.75, specificity of 0.97, and area under the receiver operating characteristic curve of 0.99. Conclusions: This formative field study suggests that passive Wi-Fi packet sensing, combined with brief on-site observation, can be used to estimate population-level exposure opportunity around an outdoor smoking area. The method identified substantial numbers of potentially exposed passersby in a high-footfall public setting. Although the findings are site specific and preliminary, they indicate that exposure-count metrics may complement concentration-based and survey-based SHS research. Further studies incorporating repeated validation, direct pollutant monitoring, and multiple sites are needed to refine the method and strengthen its usefulness for tobacco control and public health decision-making.
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STAT+: Did AI really beat doctors at diagnosis?
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Psilocybin-Induced Brain Changes May Explain Therapeutic Effects
Researchers at University of California, San Francisco and Imperial College London have shown that a single dose of psilocybin, the psychedelic compound found in magic mushrooms, causes likely anatomical brain changes that last for up to a month after the experience.
The study, involving healthy volunteers who had never taken a psychedelic, links temporary shifts in brain “entropy”—which is the diversity of neural activity occurring in the brain—to insight. This suggests the psychedelic trip itself is important to the drug’s longer term therapeutic effects.
The researchers found that a high dose of psilocybin led to increased entropy in the minutes and hours after taking the drug. The degree of entropy predicted how much insight, or emotional self-awareness, the participants felt the next day; and this, in turn, forecasted improvements in their sense of wellbeing a month later.
The findings may help to explain psilocybin’s therapeutic effects on conditions such as depression, anxiety, and addiction. “Psychedelic means ‘psyche-revealing,’ or making the psyche visible,” said senior author Robin Carhart-Harris, PhD, the Ralph Metzner distinguished professor of neurology at UCSF. “Our data shows that such experiences of psychological insight relate to an entropic quality of brain activity and how both are involved in causing subsequent improvements in mental health. It suggests that the trip—and its correlates in the brain—is a key component of how psychedelic therapy works.” Carhart-Harris is senior and corresponding author of the team’s published paper in Nature Communications, titled “Human brain changes after first psilocybin use.”
“Psychedelics have robust effects on acute brain function and long-term behavior but whether they also cause enduring functional and anatomical brain changes is largely unknown,” the authors wrote. Psilocybin is the precursor of the compound psilocin, a serotonin receptor agonist. “Converging evidence supports a role for serotonin 2A receptor (5-HT2AR) agonism in eliciting the characteristic brain and subjective effects of this and related psychedelics in humans,” the team continued.
For their newly reported study, Carhart-Harris and colleagues carried out an exploratory, placebo-controlled, within-patient study in 28 psychedelic-naïve participants who each received a single, high-dose (25 mg) of psilocybin. The researchers used an assortment of brain imaging and brain measurement techniques, some of which were carried out during the peak of the psychedelic experience, as well as before and one-month after drug administration. “This was an exploratory, hypothesis-generating mechanistic study in healthy volunteers,” the authors noted. None of the 28 people in the study had a diagnosed mental health condition, which gave the scientists greater freedom to do more testing.
In the first part of the experiment the subjects were given a 1 mg dose of psilocybin, which the researchers regarded as a placebo, and were then monitored with EEG, which records brain activity from electrodes on the scalp. Over the next few weeks, the researchers measured their subjects’ psychological insight, wellbeing, and cognitive ability. They examined brain activity with functional MRI (fMRI) and brain connectivity with diffusion tensor imaging (DTI).
One month after the placebo, the subjects were given 25 mg of psilocybin, a dose capable of eliciting a strong psychedelic trip. During the experience, researchers again measured the subjects’ brain activity with EEG, and in the following weeks they repeated the same tests they had given after the 1 mg dose.
This enabled the scientists to compare the effects of the psychedelic trip on the brain and mind to the effects of the placebo. “The multimodal neuroimaging design allowed us to observe changes in brain function and (potential) anatomy from 1-h (EEG) to 1-month (DTI) after high-dose psilocybin,” they explained.
The investigators found that within 60 minutes of taking the 25 mg dose of psilocybin, EEG revealed higher entropy, suggesting that the brain was processing a richer body of information under the psychedelic. A month later, the researchers looked at their subjects’ brains using DTI, which measures the diffusion of water along neural tracts in the brain, and found that they were denser and had more integrity. This is the opposite of what happens in aging, which makes these tracts more diffuse.
The researchers cautioned that more work needs to be done to better understand the meaning of this finding, but the result is a never-before-seen sign of how psychedelics can change the brain. ”The inclusion of DTI enabled us to test for long-term changes in the integrity of white matter tracts post psilocybin,” the authors stated. “Results revealed decreased axial diffusivity in prefrontal-subcortical tracts 1-month post 25mg psilocybin.”
The day after the 25 mg dose, all but one of the 28 subjects rated the trip as the “single most” unusual state of consciousness they had ever experienced. The remaining person rated it as among their top five. The study participants said they had experienced more psychological insight after taking the 25 mg of psilocybin than they had after the 1 mg placebo. The subjects also reported increased wellbeing two and four weeks after the study. This was measured from responses to statements such as, “I’ve been feeling optimistic about the future,” and “I’ve been dealing with problems well.”
As the scientists noted in their paper, “A predictive relationship was also found between brain entropy and longer-term mental-health changes—namely, improved wellbeing. Improved wellbeing could be predicted directly from acute increases in brain entropy as early as 1-h post dosing.”
A month after the study the study individuals also scored better on a test of cognitive flexibility. “Psilocybin seems to loosen up stereotyped patterns of brain activity and give people the ability to revise entrenched patterns of thought,” said first author Taylor Lyons, PhD, a research associate at Imperial College London. “The fact that these changes track with insight and improved well‑being is especially exciting.”
The scientists found that the subjects who had experienced the largest increases in brain entropy in the minutes to hours after taking psilocybin were the most likely to have increased insight the next day and increased wellbeing a month later. The researchers concluded that improved wellbeing was driven by the experience of insight.
The authors suggest that the study findings could improve treatment for people with mental illness using psilocybin, for example, by ensuring that the right dosage is used to produce the right amount of brain entropy to promote insight. “We already knew psilocybin could be helpful for treating mental illness,” Carhart-Harris said. “But now we have a much better understanding of how.”
In their paper the team concluded, “The present multi-modal neuroimaging study in healthy participants sheds light on the brain effects of first-time high-dose psychedelic use and the therapeutic action of psilocybin-therapy, suggesting that therapeutically relevant changes—i.e., improved wellbeing—can be forecast via an acute human brain action, i.e., an entropic brain effect, that is well-known to relate to the psychedelic experience … Results support a role for psychological insight in mediating the causal association between the entropic brain effect and potentially enduring improvements in wellbeing.”
The post Psilocybin-Induced Brain Changes May Explain Therapeutic Effects appeared first on GEN – Genetic Engineering and Biotechnology News.
Muscle Quality and Fat Distribution Predict Mortality Risk Better than BMI
Researchers at the University Medical Center Freiburg in Germany, say that detailed measures of body composition derived from whole-body MRI scans can predict diabetes, cardiovascular events, and mortality risk better than current methods that rely on body mass index (BMI). Using MRI imaging data from more than 66,000 people, the team has developed age-, sex-, and height-adjusted reference standards that show how fat and muscle are distributed across the body and how these patterns relate to health outcomes. Their findings, published in the journal Radiology, show analysis of both the quantity and quality of skeletal muscle, along with where fat is distributed in the body, can provide a more accurate way to determine risk as opposed to weight-based methods alone.
“Many risk scores and treatment decisions still rely on BMI or waist circumference because they are simple to obtain,” said senior author Jakob Weiss, MD, PhD, an interventional radiologist at University Medical Center Freiburg. “But BMI does not reliably reflect a person’s actual body composition.” This is one of the central findings of the study: that individuals with similar BMI values can have markedly different distributions of fat and muscle, which carry different levels of risk for cardiometabolic disease and mortality.
The team’s retrospective study analyzed whole-body MRI scans from 66,608 people using data from the UK Biobank and the German National Cohort collected between April 2014 and May 2022. The cohort had a mean age of 57.7 years and an average BMI of 26.2. Using a fully automated deep learning framework, the researchers quantified multiple body composition measures, including subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, skeletal muscle fat fraction, and intramuscular adipose tissue. These measures were normalized for age, sex, and height. A score is developed from these data to show how far individuals deviated from a population-adjusted reference.
“Whole-body MRI–derived BC (body composition) z-scores were used to identify at-risk individuals and predict cardiometabolic outcomes and mortality beyond traditional risk factors.” They then used the z-score categories to assess associations and clinical outcomes.
Their data showed that individuals with high visceral fat had a 2.26-fold increased risk of developing diabetes. High intramuscular fat was associated with a 1.54-fold increased risk of major adverse cardiovascular events, while low skeletal muscle was linked to a 1.44-fold increase in all-cause mortality.
The deep learning system used to develop the risk profiles was trained and evaluated against radiologist-defined reference standards, allowing it to extract volumetric measurements across the entire body rather than relying on single cross-sectional slices. This method allowed the researchers to capture meaningful variations in muscle quality and fat distribution that are not visible through other techniques.
“Manual BC measurement in large-scale imaging datasets is prohibitively time-consuming,” the researchers wrote. “However, recent advances in deep learning have enabled fully automated, accurate, and efficient quantification from cross-sectional imaging.” This capability allowed the team to construct reference curves reflective of how body composition changes with age and differs between men and women.
Importantly, the research shines a light on the limitations of using BMI to determine future risk. Because BMI is calculated using only two metrics, height and weight, it does not distinguish between fat and muscle or account for where fat is stored. Because of this, two people with the same BMI may have very different levels of visceral fat or muscle mass, factors that can lead to different to different health risks. The researchers showed that deviations in these specific components, captured via their MRI-based z-scores, were predictive of outcomes even after accounting for traditional risk factors.
“It’s not only how much muscle you have, but also it’s the quality of that muscle,” said first author Matthias Jung, MD, a radiologist at University Medical Center Freiburg. “Knowing the volume of intramuscular fat gives us a window into muscle quality that other methods like BMI, bioelectrical impedance analysis, or DEXA can’t easily provide.” This distinction is relevant because intramuscular fat is linked to metabolic dysfunction and cardiovascular risk.
The study also produced a web-based calculator that allows clinicians and researchers to compare individual patient data with population-based reference values. According to Weiss, this tool could be applied to existing imaging studies. “A dedicated whole-body MRI is not necessarily required. If a routine CT or MRI body scan already exists, the information can be extracted for benchmarking against the reference values,” he said.
The study has limitations, including a cohort of primarily White Western European adults, which may impact the generalizability of the findings. The researchers also pointed out that whole-body MRI is not routinely performed in clinical practice, although they provided reference values for commonly imaged regions such as the chest, abdomen, and pelvis to address this.
The team will continue their work by seeking to validate the reference curves in clinical populations and exploring their use in predicting treatment outcomes, including toxicity, survival, and recurrence in cancer patients. The team also plans to develop disease-specific reference values for broader patient groups to broaden the use of body composition analysis into clinical care.
The post Muscle Quality and Fat Distribution Predict Mortality Risk Better than BMI appeared first on Inside Precision Medicine.
Genotype-Guided Antidepressants Could Have Long-Term Benefits
Prescribing antidepressants according to a patient’s genetic makeup could help manage depressive symptoms in the long-term, a clinical trial suggests.
The findings, in JAMA Network Open, suggest pharmacogenetic guidance could have extended benefits, which may not be apparent early on.
Primary results did not indicate that genotype-guided SSRI treatment was better than usual care at three months in A Depression and Opioid Pragmatic Trial in Pharmacogenetics (ADOPT PGx).
However, significantly more patients receiving genotype-guided therapy achieved the secondary endpoint of depression remission at six months.
“Although outcomes were similar early in treatment, differences emerged over time,” noted Kathryn Blake, PharmD, from Nemours Center for Pharmacogenomics and Translational Research in Jacksonville, Florida, and colleagues.
“These findings suggest a possible longer-term clinical benefit and indicate that future studies should focus on evaluating the durability and long-term impact of genotype-guided prescribing in the management of depressive symptoms.”
SSRIs are the most common pharmacotherapy for depression and variants in cytochrome P450 enzymes CYP2D6 and CYP2C19 can affect their metabolism, influencing exposure to this medication.
Indeed, guidelines from the Clinical Pharmacogenetics Implementation Consortium (CPIC) provide recommendations for SSRI prescribing when genotype information is available.
However, most psychiatry experts and practice guidelines for treating depression have not yet endorsed pharmacogenetic-informed therapy, citing insufficient evidence.
ADOPT PGx was a set of three randomized clinical trials, designed to test whether routine use of pharmacogenetic testing improves medication response among patients with depression, acute pain, or chronic pain.
The ADOPT PGx Depression trial included 221 children and 1239 adults, aged eight years or older who had experienced depression for three months or longer.
A total of 692 patients (47.4%) had an actionable phenotype, of whom 351 (50.7%) were assigned to the intervention, and 341 (49.3%) to usual care.
Among this group, two-thirds reported having depressive symptoms for more than two years, and three quarters were female. The vast majority were on pharmacologic treatment, at 87.1%, with just over half receiving nonpharmacologic treatment.
Participants were randomly assigned to genotype-guided SSRI prescribing or usual care to examine whether pharmacogenetic guidance improves depression over six months.
At three months, there were no significant differences between the intervention and usual care groups in the primary endpoint of change in Patient-Reported Outcomes Measurement Information System (PROMIS) depression T scores among patients with an actionable phenotype.
At this timepoint, there were also no differences in the secondary outcome of adverse effect severity.
However, another secondary endpoint of depression remission according to a PROMIS depression T-score of 16 or less was more likely with the intervention than usual care, at 48.3% (153 of 317 patients) versus 39.4%. (122 of 310 patients).
Based on this, the authors proposed: “These findings suggest that pharmacogenetic testing, including evaluation for CYP2D6 enzyme inhibition (phenoconversion), may offer meaningful benefit with longer follow-up.”
The post Genotype-Guided Antidepressants Could Have Long-Term Benefits appeared first on Inside Precision Medicine.
STAT+: Pharma’s reputation among patient groups rose last year, but concerns remain over access and pricing
The pharmaceutical industry saw its reputation among patient groups inch up last year, but the rise masks fresh concerns about the extent to which some companies are sufficiently focusing on patient needs, according to a new survey.
Of more than 2,400 groups queried, 57% reported that drugmakers had an “excellent” or “good” reputation as they went about the business of developing and providing medicines. That was up a notch from 56% in 2024 and back to the level seen the previous year. Even so, the results place the industry below the 60% rating in 2022.
The biggest factors contributing to the slight turnabout were patient centricity — which refers to prioritizing patient needs — and ensuring patient safety, according to PatientView, a research firm that canvassed patient groups from 35 countries between December 2025 and March 2026. The firm rated the reputation of 47 companies.