Healthcare

Background. People with lived/living experience of health conditions, as well as caregivers, are increasingly engaged in research. This study aimed to develop and pilot test a new tool measuring the impact of lived/living experience engagement on the research. The measure is called the Measure of Engagement Tool for Research and lived Experience (METRE). Method. We conducted a qualitative descriptive study among 28 people with lived/living experience and caregivers and 12 academic researchers to understand the impacts of engagement. Using the findings, we drafted the METRE. We pilot tested the METRE among 13 people with lived/living experience and caregivers and 10 academic researchers. Insights were used to refine the scale. Results. Qualitatively, participants identified multiple domains of impact of engagement on research, which guided scale development. Pilot testing of the draft METRE revealed it being straightforward to complete, providing a thorough evaluation of the impact of engagement. However, some areas of improvement were recommended. The draft items showed acceptable preliminary performance. Conclusions. An assessment tool is now available to assess the impact of lived/living experience engagement on the research. Additional research is required to evaluate its psychometric properties. Tools to evaluate the impact of engagement on research will help advance the science of engagement and support engaged research teams in their work.

Background & Aims: Accurate assessment of clinical malnutrition using anthropometric and functional indicators could improve the care of elderly trauma patients in intensive care units (ICUs). This study aimed to develop an AI-driven malnutrition assessment toolbox based on a minimal set of clinically feasible indicators. Methods: Multiple machine learning models, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, XGBoost, and neural-network-based ensemble models, were developed using different indicator configurations from a clinically collected patient dataset. Models were trained using baseline and longitudinal measurements to predict malnutrition risk. SHAP analysis was used to interpret the importance of selected indicators. Results: Baseline (Day 1) data alone did not provide a reliable prediction, whereas longitudinal measurements substantially improved performance. Models based on a minimal indicator set, including bilateral mid-upper arm circumference, calf circumference, and key static variables, outperformed models using the full indicator set. Tree-based methods consistently outperformed linear and distance-based models, with the three-time-point XGBoost achieving the best individual performance. Neural-network-based ensemble models further improved predictive stability. The best overall performance was achieved by the ensemble model using the minimal indicator set from Day 1 and Day 3. SHAP analysis confirmed the importance of the selected indicators. Conclusions: This AI-driven toolbox provides an efficient and clinically feasible approach for early malnutrition assessment in elderly trauma patients in the ICU. Its strong performance with a minimal indicator set supports its potential for integration into clinical workflows and future digital twin systems for intelligent nutritional management.

Background: Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods: A search strategy in Ovid MEDLINE identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on socio-demographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analysed using descriptive statistics and thematic content analysis. Results: The survey received 716 responses. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (57.02%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Discussion: By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, and guide future policies and collaboration among researchers.

Abstract Objective: Frailty is an important concern in old age. Inflammation can cause frailty. Anti-inflammatory food supplements can play a role in slowing down frailty processes and consequences. This study explored the views of people (aged 50-89 years) on the need to develop a frailty supplement, preferences for its form and how older people could be encouraged to use such a supplement. Design: We conducted semi-structured qualitative interviews and used a framework method to analyse the data. Participants: 30 participants from a city in the UK. Setting: These participants were recruited from social housing, care homes, foodbanks and the wider population. Participants were from diverse ethnic, gender and age backgrounds. Results: Participants identified a strong need for the development of a food-based supplement for frailty. They expressed excitement for the supplement and viewed it as something which they would be happy to integrate in their daily food routine. In terms of preferences, our participants wanted to have multiple options, however, a biscuit-based supplement was preferred by most. The participants preferences were mainly based on taste of the supplement, its effectiveness, convenience in use and affordability. Muslim participants in the sample said they would be happy to use this supplement if it was developed using Halal ingredients. In terms of creating awareness and encouraging people to use the proposed supplement, participants suggested a variety of marketing methods. These included: word of mouth, face to face sessions with older adults, social media, especially YouTube and advertising on TV. Conclusion: The participants were generally open to the idea of a food-based supplement and felt that it could easily fit with their existing food practices and lifestyles. Keywords: older adults, frailty, food supplement, co-creation, healthy ageing

Global healthcare is targeting patient-centred care, as it leads to better health outcomes and higher level of patient satisfaction. Patient-centred communication, is an important part of patient-centred care because it focuses on involving patients in their care. Recent surveys both nationally and globally have shown that patients are not involved enough in their own healthcare decisions. This problem is especially common among the elderly with chronic conditions. This study aimed to describe patient-healthcare professional interactions, expectations, and satisfaction in physiotherapy within an understudied context, thereby providing important, specific data on ICE dynamics and satisfaction in the specific setting. Cross-sectional study of participants in scheduled consultations was conducted. Two government physiotherapy centres, seven private physiotherapy centres and two trust centres with physiotherapy facilities in Gujarat, India. 232 patients (from various public and private physiotherapy clinics) participated in the study. Patients' ideas, concerns, expectations (ICE) and satisfaction were explored. Almost 88% of patients reported their thoughts and explanations about their symptoms during the consultation. Most patients described not having any concerns about the diagnosis/treatment, and more than two-third of patients consulting PTs expected explanation for their symptoms. Almost 90% patients were satisfied with the consultation. The study revealed that while most patients conveyed their thoughts during consultations, very few expressed their concerns. Overall, patients were satisfied with their consultations.

While vaccination conflicts have become apparent, physicians' attitudes toward those with differing views remain unclear. Through an online survey of 492 physicians and 5,252 members of the general public in Japan in February 2026, we investigated attitudes toward four vaccines (influenza, measles, HPV, and COVID-19). Intergroup bias was assessed as ingroup minus outgroup attitudes using a feeling thermometer. Multilevel regression examined associations with agreement group and physician status. Intergroup bias was significantly positive in both agreement and disagreement groups across all vaccine types, and was higher in the agreement group. Physicians exhibited higher intergroup bias than the general public. These findings indicate that vaccination conflict is bidirectional: physicians, often viewed as targets of hostility from vaccine-hesitant individuals, themselves exhibit greater intergroup bias toward those with opposing views. Interventions to raise physicians' awareness of their own bias, alongside communication strategies for vaccine-hesitant individuals, are needed.

Background: Clinical documentation and information retrieval consume over half of physicians working hours, contributing to cognitive overload and burnout. While artificial intelligence offers a potential solution, concerns over hallucinations and source reliability have limited adoption at the point of care. Objective: To evaluate clinician-reported time savings, decision-making support, and satisfaction with DR. INFO, an agentic AI clinical assistant, in routine clinical practice. Methods: In this prospective, single-arm pilot study, 29 clinicians across multiple specialties in Portuguese healthcare institutions used DR. INFO v1.0 over five working days within a two-week period. Outcomes were assessed via daily Likert-scale evaluations and a final Net Promoter Score. Non-parametric methods were used throughout. Results: Clinicians reported high perceived time saving (mean 4.27/5; 95% CI: 3.97-4.57) and decision support (4.16/5; 95% CI: 3.86-4.45), with ratings stable across all study days and no evidence of attrition bias. The NPS was 81.2, with no detractors. Conclusions: Clinicians across specialties and career stages reported sustained satisfaction with DR. INFO for both time efficiency and clinical decision support. Validation in larger, controlled studies with objective outcome measures is warranted. Keywords: Medical AI assistant, LLMs in healthcare, Agentic AI, Clinical decision support, Point of care AI

Background Conventionally, infertility has been regarded as primarily a female issue, leading to misconceptions, stigma, and underrepresentation of male infertility in healthcare discussions. This study assessed the knowledge, attitude and perception of Undergraduates towards male infertility in Osun State University. Methods A descriptive cross-sectional design was employed to select 300 undergraduates via multistage sampling. Qualitative data were collected using a focus group discussion guide covering the knowledge, attitude and perception, while quantitative data were collected using a self-administered questionnaire covering socio-demographic characteristics, knowledge, attitude and perception towards male infertility. Qualitative analysis was performed using NVivo software, while IBM SPSS Statistics version 27 was used for the quantitative analysis, with thematic analysis and chi-square tests to determine the association between variables (significance at p < 0.05). Results Respondents were predominantly females (64.0%) with a mean age of 20.99 {+/-} 2.31 years. Overall knowledge was low (47.7%), while more than half had a negative attitude (52.3%). Significant predictors of attitude include faculty (0.049), level (p=0.031), and formal education on male infertility (p=0.007). Conclusion Students demonstrated a poor understanding of male infertility, and their attitudes remain influenced by cultural norms surrounding marriage, masculinity, and gender roles. Hence, the need to foster open dialogues, promote gender-inclusive narratives, and strengthen healthcare support systems.

Background: Large Language Models (LLMs) have demonstrated strong performance in medical question-answering tasks, highlighting their potential for clinical decision support and medical education. However, their effectiveness in subspecialty areas such as nephrology remains underexplored. In this study, we assess the performance of open-source LLMs in answering multiple-choice questions from the Nephrology Self-Assessment Program (NephSAP) to better understand their capabilities and limitations within this specialized clinical domain. Methods: We evaluated the performance of five open-source large language models (LLMs): PodGPT which a podcast-pretrained model focused on STEMM disciplines, Llama 3.2-11B, Mistral-7B-Instruct-v0.2, Falcon3-10B-Instruct, and Gemma-2-9B-it. Each model was tested on its ability to answer multiple-choice questions derived from the NephSAP. Model performance was quantified using accuracy, defined as the proportion of correctly answered questions. In addition, the quality of the models explanatory responses was assessed using several natural language processing (NLP) metrics: Bilingual Evaluation Understudy (BLEU), Word Error Rate (WER), cosine similarity, and Flesch-Kincaid Grade Level (FKGL). For qualitative analysis, three board-certified nephrologists reviewed 40 randomly selected model responses to identify factual and clinical reasoning errors, with performance summarized as average error ratios based on the proportion of error-associated words per response. Results: Among the evaluated models, PodGPT achieved the highest accuracy (64.77%), whereas Llama showed the lowest performance with an accuracy of 45.08%. Qualitative analysis showed that PodGPT had the lowest factual error rate (0.017), while Llama and Falcon achieved the lowest reasoning error rates (0.038). Conclusions: This study highlights the importance of STEMM-based training to enhance the reasoning capabilities and reliability of LLMs in clinical contexts, supporting the development of more effective AI-driven decision-support tools in nephrology and other medical specialties.

Abstract The ISBAR framework is used to standardize clinical handovers and enhance patient safety. Observational tools based on ISBAR have been developed to assess the completeness of information transfer. However, these instruments have primarily been developed in non-Chinese contexts, and validated Chinese-language observational tools suitable for clinical practice remain limited. In this study, a cross-cultural adaptation and psychometric validation of the ISBAR Structured Handover Observation Tool was conducted, examining its reliability and discriminant validity in Chinese clinical settings. The study was conducted in two phases: cross-cultural adaptation and psychometric evaluation in real-world clinical settings. Content validity was assessed using the Content Validity Index (CVI), and inter-rater reliability was evaluated using the Intraclass Correlation Coefficient (ICC) based on a two-way mixed-effects model with absolute agreement. Discriminant validity was examined using the Mann-Whitney U test to compare scores across nurses with varying levels of clinical experience. A total of 233 handover cases involving patient transfers from the intensive care unit (ICU) to general wards were collected, involving 84 nurses. The scale demonstrated good content validity, with item-level content validity indices (CVI) ranging from 0.88 to 1.00 and a scale-level CVI/Ave of 0.98. The inter-rater reliability, assessed using fifty randomly selected cases, was high, with an intraclass correlation coefficient (ICC) of 0.885 for single-rater assessments and 0.939 for average-rater assessments. Discriminant validity analysis showed that nurses with more clinical experience had significantly higher total scores than those with less experience (Z = -4.772, p < 0.001). The Chinese version of the ISBAR Structured Handover Observation Tool demonstrates good content validity, high inter-rater reliability, and acceptable discriminant validity. This tool provides a standardized and practical method for assessing the completeness of information transfer and is expected to support quality improvement in patient handover from the ICU to general wards in Chinese clinical settings.

BackgroundPhysical activity has been associated with better reading comprehension and reduces cognitive load (CL), but the role of brain volume in modulating this relationship remains unclear. Therefore, this study aims to determine whether the gray matter volume in key regions modulates the effects of different physical activity modalities on reading comprehension and associated CL. MethodsThirteen male adolescents (12-13 years). Adolescents with MRI data participated in a randomized cross-over trial comparing three conditions: 1) sedentary behavior (SC, emulating a school class), 2) moderate-intensity continuous training (MICT), and 3) cooperative high-intensity interval training (C-HIIT), with physical activity conditions duration adjusted to match SC energy expenditure. Gray matter volumes were measured in the bilateral hippocampus, left pars opercularis, and the brainstem. CL was assessed via pupil dilation during reading using eye-tracking. Reading comprehension was measured through seven-question multiple-choice tests with expert-validated items. ResultsC-HIIT demonstrated superior effects on both CL and reading comprehension compared to MICT and SC, with significant brain volume modulation effects across all examined regions. Brain volume interactions with physical activity modalities systematically modified the pattern of cognitive responses, with C-HIIT consistently benefiting from these modulations, whereas the effects of MICT were generally attenuated. ConclusionThis study suggests that selecting the appropriate physical activity modality may be relevant for cognitive outcomes during reading in adolescents. C-HIIT yielded lower CL and better reading comprehension, and these effects were not explained by brain volume alone but by its interaction with exercise modality.

Background: Accurate dietary assessment is essential for precision nutrition and effective nutrition surveillance. However, portion size estimation remains a persistent challenge, particularly in culturally diverse regions such as Central Asia. Traditional self-reporting tools often yield inconsistent results due to communal eating practices and unfamiliarity with standard measures. Objective: To address these limitations, this study aimed to compare three methods: unassisted human judgment, visual food atlas assistance, and an artificial intelligence (AI) model, using Central Asian food items. Methods: In this cross-sectional study, 128 participants from Astana, Kazakhstan, visually estimated portion sizes of 51 foods and 8 beverages from standardized photographs. Participants were randomized into two groups: one using unassisted visual estimation and the other aided by a regionally tailored digital food atlas. Additionally, an AI model trained on Central Asian food images was evaluated. Actual food weights served as the reference standard. Accuracy was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) across food types and portion sizes. Results: The atlas-assisted group demonstrated the highest accuracy, with the lowest MAE (80.81g) and MAPE (44.76%) across all portions. The AI model showed promising results for average portions (MAE: 79.07g, MAPE: 67.91%) but underperformed on small portions, particularly for meat-based items. Unassisted estimates were the least accurate (MAE: 133.86g, MAPE: 79.40%). Across food categories, visual aids consistently improved accuracy, while AI demonstrated variability by texture and portion size. Conclusions: Culturally adapted visual atlases significantly enhance portion size estimation accuracy in non-Western, communal-eating contexts. While AI models hold promise for dietary assessments, particularly with standard portions and beverages, further refinement is needed for complex food items and small portion types. These findings support the integration of visual and AI-based tools into region-specific dietary monitoring strategies.

Recognizing the challenges faced by primary caregivers regarding the health of children with congenital craniofacial anomalies (CCAs) contributes to strengthening healthcare programs according to patient[s] and families differential needs. This qualitative study presents the experiences of 25 caregivers of children with CCAs from Bogota and Cali, Colombia, identified from care registries and consultation statistics provideed from public high-complexity healthcare institutions. Grounded in Giorgis descriptive phenomenology and employing thematic analysis, this research utilized semi-structured interviews and focus groups to explore the diagnostic process and its impact, experiences with healthcare services, and the caregivers role and daily care activities. Data were analyzed using MAXQDA(R) qualitative software. Findings highlighted the emotional complexity of caring for childre[n]s health. Challenges included late diagnoses, pessimistic views of the children with CCAs condition by healthcare team members; lack of effective support, information, and guidance from health staff; absence of clear care and referral protocols, and limited access to specific adaptations and timely specialized care for children with CCAs. There were also reduced therapeutic services, and a pronounced gendered caregiving burden when responsibilities fall almost exclusively on mothers. System fragmentation, reflected in deficiencies in communication and a lack of clear, coordinated, and timely pathways of care, as well as the absence of adequate psychosocial support for families, emerged as common structural problems in healthcare services in both geographic settings where this research has been conducted. Gender-sensitive strategies focused on alleviating emotional concerns and the burden of caregiving from diagnosis onward within a patient and family-centered care model are decisive. Improving comprehensive CCAs training for healthcare personnel and making adjustments to care pathways are suggested to contribute to the implementation of inclusive health programs that address the diverse needs of children and their families.

Objective: A questionnaire survey was conducted on the willingness and demand for acupuncture treatment in patients with malignant tumors, and the possible factors affecting patients' willingness and demand for acupuncture treatment were explored. Methods: A voluntary, anonymous survey was conducted between February and May 2025 among patients with malignant tumors aged 18 years and older who visited Beijing Cancer Hospital. The questionnaire included 16 questions addressing three dimensions:current medical purposes,Traditional Chinese Medicine(TCM) literacy, and acupuncture treatment needs.The questionnaire was posted online and completed by respondents using a smartphone interface. Results: A total of 511 valid questionnaires were retrieved in the survey, and 481 patients(94.1%) are willing to receive acupuncture treatment. Among the 481 patients willing to receive acupuncture treatment, the top five symptoms they hoped to improve with acupuncture were: disturbed sleep (245 participants, 50.9%); pain (229 participants, 47.6%); fatigue (177 participants, 36.8%); numbness (165 participants, 34.3%); and poor appetite (144 participants, 29.9%). Among patients who chose to "explicitly accept" acupuncture treatment and those who "accepted acupuncture treatment upon doctor's recommendation", 55% and 56% respectively had good knowledge of traditional Chinese medicine (TCM) culture. In contrast, this proportion was only 36.7% among patients who refused acupuncture treatment, and the difference was statistically significant (P<0.05). The survey results also show that Female patients reported significantly higher demands for pain relief and improved sleep than male patients, with statistically significant differences (P<0.05). Furthermore, those aged 18-45 and with better TCM literacy were more likely to desire acupuncture to improve sleep, with statistically significant differences (P<0.05). Conclusion: Differences in TCM literacy can influence patients' willingness to choose acupuncture treatment. Strengthening patient health education and improving TCM literacy will help increase cancer patients' willingness to choose TCM acupuncture treatment, thereby enabling them to benefit from acupuncture. For patients aged 18-45, those with good TCM literacy female with high acupuncture needs, acupuncture treatment may be recommended as a priority.

BackgroundAdequate maternal nutritional knowledge is essential for healthy pregnancy outcomes, yet many pregnant women lack good nutritional knowledge. This study assessed nutritional knowledge and associated factors among pregnant women in the Krowor Municipality of Ghana. MethodsA facility-based cross-sectional study was conducted among pregnant women attending antenatal clinics in two public health facilities. Structured questionnaires were used to collect data on sociodemographic characteristics and nutritional knowledge. Data were analysed using descriptive statistics and chi-square tests at a 5% significance level. ResultsMost respondents demonstrated moderate nutritional knowledge (mean score =11.24 {+/-} 2.48), with 45% classified as having moderate knowledge. Income level (p = 0.00), education (p = 0.007), gestational age (p = 0.042), employment status (p = 0.007), and religion (p = 0.005) were significantly associated with nutritional knowledge. ConclusionThe study highlights notable gaps in nutritional knowledge among pregnant women in Krowor Municipality. Socioeconomic and obstetric factors strongly influenced nutritional knowledge. Strengthening antenatal nutrition counselling and improving socioeconomic support may help improve the nutritional knowledge of pregnant women.

Background: Artificial intelligence chatbots (AICs), as a form of generative artificial intelligence (AI), are increasingly being considered for use in scholarly peer review to assist with tasks such as identifying methodological issues, verifying references, and improving language clarity. Despite these potential benefits, concerns remain regarding their reliability, ethical implications, and transparency. Evidence on how medical journal peer reviewers perceive the role and impact of AICs is limited. This study explored reviewers' familiarity with AICs, perceived benefits and challenges, ethical concerns, and anticipated future roles in peer review. Methods: We conducted a cross-sectional online survey of medical journal peer reviewers. Corresponding author information was extracted from MEDLINE-indexed articles added to PubMed within a two-month period using an R-based approach. A total of 72,851 authors were invited via email to participate; those who self-identified as peer reviewers were eligible. The 29-item survey assessed familiarity with AICs and perceptions of their benefits and limitations in peer review. The survey was administered via SurveyMonkey from April 28 to June 16, 2025, with two reminder emails sent during the data collection period. Results: A total of 1,260 respondents completed the survey. Most participants were familiar with AICs (86.2%) and had used tools such as ChatGPT for general purposes (87.7%), but the majority had not used AICs for peer review (70.3%). Most respondents reported that their institutions do not provide training on AIC use in peer review (69.5%), although many expressed interest in such training (60.7%). Perceptions of AIC benefits were mixed, while concerns were widely shared, particularly regarding potential algorithmic bias (80.3%) and issues related to trust and user acceptance (73.3%). Conclusions: While familiarity with AICs is high among medical journal peer reviewers, their use in peer review remains limited. There is clear interest in training and guidance, however, concerns related to ethics, data privacy, and research integrity persist and should be addressed before broader implementation.

Background: Extrauterine growth restriction (EUGR) is a common and clinically significant complication among preterm infants, contributing to adverse neurodevelopmental and metabolic outcomes. Early and individualized risk prediction remains challenging. This study aimed to develop and validate an interpretable machine learning model for early prediction of EUGR using routinely available clinical variables, and to implement a user-friendly web-based calculator for clinical use. Methods: We retrospectively analyzed 1,431 preterm infants admitted within 24 hours after birth to our hospital between May 2020 and March 2025. Infants from the Yangpu campus (n=863) formed the training set, and those from the Huangpu campus (n=568) formed the validation set. Early clinical variables available within 48-72 hours were screened using the Boruta algorithm. Logistic regression, XGBoost, random forest, decision tree, and support vector machine models were developed and compared. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Brier score. SHapley Additive exPlanations (SHAP) were applied to assess global and individual feature contributions, nonlinear effects, and interactions. A web-based calculator was constructed based on the optimal model. Results: Nine variables were identified as important predictors: birth weight, small for gestational age status, gestational age, breastfeeding, multiple gestation, neonatal respiratory distress syndrome, patent ductus arteriosus, maternal hypertension, and maternal group B Streptococcus infection. Among the five models, XGBoost achieved the best performance in the validation set (AUC 0.922, accuracy 0.849, Brier score 0.108). SHAP analysis showed that low birth weight, small for gestational age, maternal group B Streptococcus infection, and patent ductus arteriosus were major risk factors, while breastfeeding was protective. Notable nonlinear and interactive effects were observed, particularly between birth weight and gestational age and between breastfeeding and patent ductus arteriosus. The web-based calculator provides real-time individualized risk estimation and visualized interpretation. Conclusions: An interpretable XGBoost-based model and web calculator were successfully developed and validated for early prediction of EUGR in preterm infants. This tool may support clinicians in identifying high-risk infants and guiding individualized nutritional and clinical management.

Social determinants of health (SDoH) are important for clinical care, but it remains unclear how much AI-captured social context is preserved after clinician editing in ambient documentation workflows. We retrospectively analyzed 75,133 paired ambient AI-drafted and clinician-finalized note sections from ambulatory care at a large academic health system. Using a rule-based NLP pipeline, we extracted 21 SDoH categories and quantified retention, deletion, and addition. SDoH appeared in 25.2% of AI drafts versus 17.2% of final notes. At the mention level, AI captured 29,991 SDoH mentions, of which 45.1% were deleted, 54.9% were retained with clinicians adding 3,583 new mentions. Insurance and marital status were most often deleted, whereas substance use and physical activity were more often retained. Deletion patterns also varied by specialty, supporting the need for specialty-aware ambient AI systems.

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in behavior management (PTBM) as first-line treatment. However, assessing guideline adherence through manual chart review is time-consuming and costly, limiting scalable and timely quality-of-care measurement. ObjectiveTo evaluate the accuracy and explainability of large language models (LLMs) in identifying PTBM recommendations in pediatric electronic health record (EHR) notes as a scalable alternative to manual chart review. Design, Setting, and ParticipantsThis retrospective cohort study was conducted in a community-based pediatric healthcare network in California consisting of 27 primary care clinics. The study cohort included children aged 4-6 years with [&ge;] 2 primary care visits between 2020-2024 and ICD-10 diagnoses of ADHD or ADHD symptoms (n=542 patients). Clinical notes from the first ADHD-related visit were included. A stratified subset of 122 notes, including all cases with model disagreement, was manually annotated to assess model performance in identifying PTBM recommendations and rank model explanations. ExposuresAssessment and plan sections of clinical notes were analyzed using three generative large language models (Claude-3.5, GPT-4o, and LLaMA-3.3-70B) to identify the presence of PTBM recommendations and generate explanatory rationales and documentation evidence. Main Outcomes and MeasuresModel performance in identifying PTBM recommendations (measured by sensitivity, positive predictive value (PPV), and F1-score) and qualitative explainability ratings of model-generated rationales (based on the QUEST framework). ResultsAll three models demonstrated high performance compared to expert chart review. Claude-3.5 showed balanced performance (sensitivity=0.89, PPV=0.95, and F1-score=0.92) and ranked highest in explainability. LLaMA3.3-70B achieved sensitivity=0.91, PPV=0.89, and F1-score=0.90, ranking second for explainability. GPT-4o had the highest PPV [0.97] but lowest sensitivity [0.82], with an F1-score of 0.89 and the lowest explainability ranking. Based on classifications from the best-performing model, Claude-3.5, 26.4% (143/542) of patients had documented PTBM recommendations at their first ADHD-related visit. Conclusions and RelevanceLLMs can accurately extract guideline-concordant clinician recommendations for non-pharmacological ADHD treatment from unstructured clinical notes while providing clear explanations and supporting evidence. Evaluating model explainability as part of LLM implementation for medical chart review tasks can promote transparent and scalable solutions for quality-of-care measurement.

Introduction Cesarean delivery accounts for nearly one-third of U.S. births and is associated with substantial maternal morbidity and health care costs. Persistent racial disparities have been documented, yet the structural factors contributing to these differences remain incompletely understood. The extent to which insurance coverage shapes racial disparities in cesarean delivery remains unclear. Objective To evaluate the independent and interactive associations of race/ethnicity and insurance coverage with cesarean delivery in the United States. Methods Population-based retrospective cohort study using singleton live births recorded in the United States Vital Statistics Natality files from 2014 to 2024. Multivariable logistic regression was used to estimate the independent effects of race/ethnicity and insurance status on cesarean delivery, including interaction terms to test effect modification, using national birth certificate data. Models were adjusted for maternal demographics, clinical factors, and temporal covariates. Adjusted odds ratios, predicted probabilities, and absolute risk differences were derived from post-estimation marginal effects. The main outcome measure was cesarean delivery (yes vs no). Results Among 41,543,568 deliveries from 2014 to 2024, 13,312,221 (32.0%) were cesarean deliveries. After adjustment, both race and ethnicity and insurance status were independently associated with cesarean delivery. Compared with non-Hispanic White women, non-Hispanic Black women had higher odds of cesarean delivery (odds ratio [OR], 1.22; 95% CI, 1.22-1.23). Relative to uninsured women, those with private insurance had 59% higher odds of cesarean delivery (OR, 1.59; 95% CI, 1.58-1.60). Significant interaction effects were observed, indicating that insurance coverage modified racial and ethnic differences in cesarean delivery. Non-Hispanic Black women had the highest predicted probabilities across all insurance categories, with the largest absolute disparities observed among uninsured women. Conclusion Racial and ethnic differences in cesarean delivery persist in the United States and are modified by insurance coverage, suggesting that coverage-related differences may contribute to inequities in obstetric care.