Tag: pubmed

JMIR Form Res. 2026 Jul 7;10:e96755. doi: 10.2196/96755.

ABSTRACT

BACKGROUND: Manual chart abstraction from electronic health records is a critical step in clinical outcomes research but is time-intensive and prone to human error. Advances in artificial intelligence (AI), particularly large language models, offer the potential to automate the extraction of structured data from unstructured clinical documentation with improved efficiency and consistency.

OBJECTIVE: This study aimed to evaluate the accuracy and efficiency of an AI-assisted approach for extracting patient-reported outcomes from clinical notes compared with traditional human abstraction.

METHODS: We conducted a retrospective study of 26 patients treated with low-dose radiation therapy for osteoarthritis. Human reviewers abstracted numeric rating scale (NRS; 0-10) pain scores at baseline, the end of treatment, and the first follow-up, and von Pannewitz score (VPS; 0-4) improvement scores at posttreatment time points. A HIPAA (Health Insurance Portability and Accountability Act)-compliant generative pretrained transformer-based AI system was prompted to extract the same end points from clinical notes. Concordance was assessed using exact match rates, the intraclass correlation coefficient for the NRS, and weighted Cohen κ for the VPS. The time required for AI vs manual abstraction was recorded. The AI system was not trained or fine-tuned on study data, and performance was evaluated directly against human abstraction to reflect real-world deployment.

RESULTS: The AI system demonstrated high concordance with human abstraction, achieving an exact match rate of 92% for the NRS (95% CI 84-96; intraclass correlation coefficient=0.96) and 94% for the VPS (95% CI 84-98; κ=0.91). All discrepancies were minor, and no spurious values were generated. The AI system identified 1 clinically relevant data point missed during manual review. Average abstraction time per patient decreased from approximately 30 minutes to 2 minutes, representing time savings of >90%. The system also captured trends in analgesic use, but these results were not statistically significant, including reductions without escalation.

CONCLUSIONS: AI-assisted data abstraction demonstrated high concordance with human review in this single-institution cohort while substantially reducing the time requirements. These findings support the feasibility of AI-assisted abstraction workflows, although further validation across larger and more diverse datasets is needed.

PMID:42412398 | DOI:10.2196/96755

Neurol Ther. 2026 Jul 7. doi: 10.1007/s40120-026-00991-3. Online ahead of print.

ABSTRACT

INTRODUCTION: In the course of their disease, most patients with Parkinson’s disease (PD) will experience OFF episodes, during which symptoms worsen despite symptomatic treatment. Current on-demand treatments for OFF episodes include inhaled levodopa and subcutaneous and sublingual apomorphine, however no head-to-head comparison of these treatments is available. We performed a network meta-analysis (NMA) to provide robust comparative evidence for on-demand OFF episode treatments.

METHODS: Randomized controlled trials assessing OFF episode treatments in patients with PD were identified in a systematic literature review. A feasibility assessment was conducted considering OFF time, Unified Parkinson’s Disease Rating Scale (UPDRS) Part III scores, Patient Global Impression of Change score (PGI-C), and safety outcomes. An NMA was carried out using a random effects model.

RESULTS: Twenty-one trials were identified in the systematic literature review, 11 of which were included in the feasibility assessment and deemed suitable to be included in the NMA. No statistically significant difference in OFF time was observed between patients receiving inhaled levodopa and those receiving subcutaneous apomorphine, and no statistically significant difference in UPDRS Part III scores or probability of PGI-C score improvements, all-cause discontinuation, or adverse events (AEs) were observed between patients receiving inhaled levodopa, subcutaneous apomorphine, or sublingual apomorphine. Subcutaneous apomorphine had significantly higher probability of treatment discontinuation due to adverse events compared to inhaled levodopa (Log odds ratio 20.712; 95% credible interval 1.855, 55.991).

CONCLUSION: Inhaled levodopa demonstrated no statistically significant difference in efficacy with subcutaneous or sublingual apomorphine, however inhaled levodopa had a lower probability of treatment discontinuation due to AEs than subcutaneous apomorphine. These data highlight that inhaled levodopa is a suitable non-invasive and well-tolerated treatment for OFF episodes.

PMID:42412388 | DOI:10.1007/s40120-026-00991-3

Odontology. 2026 Jul 7. doi: 10.1007/s10266-026-01498-x. Online ahead of print.

ABSTRACT

This study aimed to evaluate the clinical safety, informational completeness, and patient-centered language of responses generated by four large language model (LLM)-based systems to standardized periodontal complaint-based queries. Seven standardized symptom-based periodontal queries were developed based on commonly reported patient complaints and submitted in Turkish to Copilot, Gemini, Claude, and ChatGPT. Responses were evaluated using a structured rule-based framework consisting of a Content Coverage Score (CCS), Risk of Harm Score (RHS), and Patient Language Score (PLS). Assessments were performed by two blinded human reviewers and one blinded AI-based evaluator. Human consensus, AI consensus, and combined evaluator scores were calculated. Agreement between evaluators was assessed using intraclass correlation coefficients (ICC), while human-AI differences and inter-model comparisons were analyzed using non-parametric statistical tests. Excellent agreement was observed between human reviewers, repeated AI evaluations, and human-AI consensus scores (ICC range: 0.911-0.925; p < 0.001). No significant difference was found between overall human and AI consensus scores (p = 0.985). Across the four AI systems, no statistically significant differences were observed in CCS or PLS scores in the human, AI, or combined evaluator analyses (all p > 0.05). PLS scores were generally high across models, indicating good linguistic accessibility for patients. No clearly harmful guidance was identified by the human reviewers. Overall, LLM-based systems generated clinically safe, reasonably comprehensive, and generally patient-accessible responses to common periodontal complaint-based queries. Although these systems may serve as supplementary sources of periodontal health information, they cannot replace individualized clinical evaluation and professional dental consultation.

PMID:42412385 | DOI:10.1007/s10266-026-01498-x

Ther Innov Regul Sci. 2026 Jul 7. doi: 10.1007/s43441-026-01012-z. Online ahead of print.

ABSTRACT

The ICH E9(R1) estimands framework requires precise specification of the treatment effects (estimands) a trial is designed to estimate. A recent article by Troxel et al. [1] has advanced a narrow view that only estimands using the treatment-policy strategy are scientifically defensible. In particular, the article recommends that journals adopt a new policy with regard to reporting results from clinical trials, advocating that only results based on the treatment policy strategy should appear in the main body of the paper with estimates based on other strategies relegated to supplementary materials. Treatment-policy estimands target the effect of assignment to treatment and are defined to include outcomes after non-terminal post-randomization events, such as treatment discontinuation or initiation of alternative or rescue medications. Use of this estimand requires that outcome collection continues following these intercurrent events. In the presence of missing data, estimation of effects using the treatment policy strategy typically relies on strong, unverifiable assumptions [2]. While using treatment policy strategies to address all intercurrent events is appropriate for certain scientific objectives, these estimands do not address all clinically relevant questions. Results based on estimands using alternative strategies for primary and key secondary objectives should therefore also be presented in the main body of the published paper when they address important clinical questions that are relevant to patient care or decision-making.

PMID:42412376 | DOI:10.1007/s43441-026-01012-z

Anal Sci. 2026 Jul 7. doi: 10.1007/s44211-026-00940-y. Online ahead of print.

ABSTRACT

Lysine methylation is an important epigenetic modification that regulates chromatin structure and gene expression. However, it is still difficult to distinguish its methylated states without labels at the single-molecule level. In this study, we investigate the discrimination of lysine methylation states using single-molecule tunneling measurements with gold nano-gap electrodes. The conductance decreases stepwise as the number of methyl groups increases, even though density functional theory (DFT) shows that all molecules have almost the same HOMO energy levels. This result suggests that conductance is not determined only by the electronic structure, but also by how the molecule is arranged between the electrodes. Statistical analysis of current signals shows that high-conductance events become less frequent after methylation, indicating fewer strongly coupled configurations. The relationship between current and molecular length also supports that transport depends on variations in molecular configurations. Machine learning analysis achieved an F-score of 0.76 for distinguishing methylated from unmethylated lysine. In contrast, distinguishing between mono-, di-, and trimethylated forms gave a lower F-score of 0.49, reflecting overlap in the signals. These results suggest that single-molecule tunneling currents are sensitive to stepwise lysine methylation states through differences in transient molecular configurations. This work demonstrates the potential of single-molecule tunneling measurements for label-free analysis of epigenetic modifications.

PMID:42412374 | DOI:10.1007/s44211-026-00940-y

Nano Converg. 2026 Jul 7;13(1):32. doi: 10.1186/s40580-026-00562-0.

ABSTRACT

This study investigates the impact of high-energy proton irradiation (33 and 100 MeV) on the radiation response and statistical uniformity of the breakdown field of Hf_xZr_1-xO₂ based metal-insulator-metal capacitors. While macroscopic electrical characteristics-such as polarization hysteresis, dielectric constant, and leakage current-exhibit remarkable radiation stability across various crystalline phases, a distinct improvement is observed in the statistical distribution of the breakdown field (E_BD). Weibull distribution analysis reveals a consistent increase in the shape factor (β) following irradiation, indicating a “healing effect” that effectively narrows the variance of dielectric breakdown. This enhancement leads to a normalized yield improvement ranging from 3.0% to 14.8%. Our findings suggest that optimized high-energy proton treatment can effectively mitigate localized defects and suppress early-stage failures. These results provide a strategic pathway for enhancing the reliability and operational lifetime of high-k dielectrics in space-qualified and radiation-hardened electronics.

PMID:42412370 | DOI:10.1186/s40580-026-00562-0

J Endocrinol Invest. 2026 Jul 7. doi: 10.1007/s40618-026-02980-7. Online ahead of print.

ABSTRACT

INTRODUCTION: The C-reactive protein to albumin ratio (CAR), an integrative biomarker of inflammation and malnutrition, has shown prognostic value in various diseases, but its role in diabetic chronic kidney disease (CKD) remains inadequately defined. This study aimed to evaluate the independent association of CAR with progression of chronic kidney disease (CKD) and all-cause mortality.

DESIGN AND METHODS: We retrospectively retrieved 231 CKD patients with diabetes who had not received erythropoietin stimulants or iron therapy. The primary outcomes were mortality and progression of CKD (composite), specifically, progression to end-stage renal disease (ESRD) and initiation of renal replacement therapy (RRT), or a doubling of serum creatinine (SCr) levels in patients not receiving RRT. We used Kaplan-Meier survival curves and constructed multivariate Cox proportional hazards models adjusted for potential confounding factors, to estimate the association of CAR with progression of CKD and all-cause mortality; in addition, we employed restricted cubic spline analysis to explore nonlinear relationships. We used the SHAP machine learning algorithm to evaluate the predictive performance of CAR and to analyze the predictive increment of CAR for clinical outcomes. Subgroup analyses were conducted to assess the robustness of the results across different subgroups and modeling choices.

RESULTS: The analysis cohort included a total of 231 adults. Kaplan-Meier curves showed a progressive and significant increase in cumulative CKD progression and mortality across CAR quartiles. In the fully adjusted model of the Cox multivariate regression analysis, a 1-unit increase in CAR (log-transformed) was associated with a 59% increase in the risk of CKD progression (HR = 1.59, 95% CI 1.20-2.09; P = 0.001) and a 32% increase in the risk of mortality (HR = 1.32, 95% CI 1.03-1.68, P = 0.029); participants in the highest quartile had a significantly higher mortality risk compared to those in the lowest quartile (Q4 vs. Q1, HR = 3.8, 95% CI 1.24-11.67; P = 0.02). Restricted cubic spline analysis revealed a significant linear relationship (nonlinear P > 0.05). Subgroup analysis indicated that CAR was consistently associated with outcomes across different age, sex, and BMI groups, with no significant interactions observed, confirming the robustness of these results. In machine learning models, SHAP analysis identified CAR as a key predictor. Compared with the baseline risk model (UTP, eGFR), adding CAR improved predictive performance for CKD progression and mortality, with enhanced C-statistic, improved discriminatory index (IDI), and improved net reclassification index (NRI).

CONCLUSIONS: The CAR serves as a robust, independent predictor of CKD progression and all-cause mortality in patients with diabetic CKD. As a readily accessible biomarker, it holds significant potential to enhance risk stratification and identify candidates warranting intensified clinical management.

PMID:42412363 | DOI:10.1007/s40618-026-02980-7

Ann Med. 2026 Dec;58(1):2699553. doi: 10.1080/07853890.2026.2699553. Epub 2026 Jul 7.

ABSTRACT

METHODS: A hospital-based cross-sectional study was conducted among hypertensive outpatients at the University of Gondar Comprehensive and Specialized Hospital from January 30 to May 30, 2024. Participants were selected using simple random sampling. Alcohol use disorder was assessed using the Alcohol Use Disorders Identification Test (AUDIT) and medication adherence was evaluated over a three-month period. Bivariable and multivariable logistic regression analyses identifed factors associated with AUD and non-adherence to antihypertensive therapy. Statistical significance was set at p < 0.05.

RESULTS: A total of 400 participants were included (response rate: 100%), with a mean age of 44.9 ± 12.5 years. The prevalence of AUD was 12.2%, comprising hazardous drinking (8.0%), harmful use (2.5%), and alcohol dependence (1.7%). Male sex (AOR = 3.60; 95% CI: 1.30-9.94), cigarette smoking (AOR = 8.56; 95% CI: 3.89-18.82), and comorbidities (AOR = 3.87; 95% CI: 1.75-8.56) were independently associated with AUD. Overall, 42.2% of participants were non-adherent to antihypertensive therapy. Alcohol dependence was associated with nearly fourfold higher odds of poor adherence compared with social drinking.

CONCLUSION: AUD is common among hypertensive patients and is significantly associated with poor adherence to antihypertensive therapy. Integrating alcohol use screening and intervention into routine hypertension care may improve medication adherence and treatment outcomes.

PMID:42411344 | DOI:10.1080/07853890.2026.2699553

Public Health Nutr. 2026 Jul 7:1-72. doi: 10.1017/S1368980026102985. Online ahead of print.

ABSTRACT

OBJECTIVE: To examine the impact of one-to-one peer support on mothers’ personal breastfeeding goals.

DESIGN: Scoping review guided by Arksey and O’Malley’s five-stage framework and reported in accordance with PRISMA-ScR guidelines. Qualitative data were analysed using descriptive content analysis. Quantitative data were analysed by identifying numerical trends and recurring patterns, and a concise overview of key descriptive findings was provided using frequency counts and proportions.

SETTING: Studies conducted across 10 countries globally, identified through systematic searches of seven electronic databases and screening of reference lists.

PARTICIPANTS: Thirty-eight studies were included: 20 quantitative, 7 qualitative, 6 mixed-methods, and three secondary analyses (drawing on two relevant primary sources). Participants were mothers who received one-to-one breastfeeding peer support, predominantly in community or home-based settings.

RESULTS: One primary outcome was assessed: The impact of one-to-one peer support on mothers’ personal breastfeeding goals. Two secondary outcomes were identified. The first examined the effect of one-to-one peer support on breastfeeding outcomes based on traditional measures of breastfeeding success. Of the included studies, 50% reported positive effects of one-to-one peer support on traditional measures of breastfeeding success, while 21% found no statistically significant differences. An additional secondary outcome reported in 34% of the included studies examined the impact of mother-centred breastfeeding peer support on maternal emotional well-being.

CONCLUSIONS: One-to-one peer support enhances the mothers’ ability to achieve their personal breastfeeding goals and positively influences emotional well-being. These findings underscore the need to integrate structured one-to-one peer support into maternal health services in Ireland and globally.

PMID:42411308 | DOI:10.1017/S1368980026102985

Womens Health (Lond). 2026 Jan-Dec;22:17455057261465645. doi: 10.1177/17455057261465645. Epub 2026 Jul 7.

ABSTRACT

BackgroundDespite increased recognition of autism in women and girls, their reproductive health remains underexplored. Understanding reproductive health burden and healthcare experiences is essential to identifying barriers and improving support for conditions that can impact quality of life.ObjectivesInvestigate reproductive health and healthcare experiences among autistic compared to non-autistic individuals assigned female at birth (AFAB).DesignWe conducted a cross-sectional online survey in the UK (April 2024-July 2025) among individuals AFAB aged 18-40 years recruited via convenience-sampling from autism networks, social media, and Prolific.MethodsIn total, 311 participants were included (165 self-reported autistic [M=31.1 years, SD=6.2], 146 non-autistic [M=30.6 years, SD=5.5]). The survey, developed with input from autistic people, covered reproductive health conditions, knowledge and management of reproductive health, and reproductive healthcare experiences. Group differences were analysed using logistic regressions, chi-squared and Wilcoxon rank-sum tests. Healthcare inequality (HIE) scores were calculated overall and for five subdomains as composite of negative reproductive healthcare experiences. Associations between autism and HIE were examined using logistic regression.ResultsAutistic participants reported more reproductive health conditions (44% vs. 28%) and symptoms (95% vs. 84%) than non-autistic participants. Age-adjusted regression models indicated higher odds for any condition (OR=1.96[1.21-3.17], p <.01) and any symptom (OR=3.23[1.44-7.25], p <.01) with OR for specific conditions/symptoms ranging from OR=1.09[0.57-2.09], p=.799 to OR=3.97[2.43-6.50], p <.001. Adjusting for other neurodivergence attenuated estimates; however, the overall associations for any symptom remained statistically significant (p <.05). Autistic participants were more likely to report irregular menstrual cycles, menstrual cycle-related mental health and sensory experiences changes and poorer reproductive health knowledge and management (all p <.001). HIE scores overall and across subcategories were higher among autistic individuals. Autism diagnosis was associated with higher overall HIE scores (OR=2.86[2.37-3.45], p <.001) and domain specific HIE scores (OR_range = 1.81[1.49-2.22]-5.31[3.56-8.13], p <.001).ConclusionAutistic individuals AFAB face increased reproductive health burden, greater difficulty managing their reproductive health, and significant healthcare inequities. Tailored education and individualized service adjustments are essential for equitable reproductive care in autistic individuals AFAB.

PMID:42411262 | DOI:10.1177/17455057261465645