Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The effective implementation of personalized pharmacogenomics (PGx) requires the integration of released clinical guidelines into decision support systems (CDSS) to facilitate clinical applications. Large language models (LLMs) can be valuable tools for automating information extraction and updates. Objective: To assess the effectiveness of repeated cross-comparisons and an agreement-threshold strategy in two advanced LLMs as supportive tools for updating information. Methods: The study evaluated the performance of two LLMs, GPT-4o and Gemini-1.5-Pro, in extracting PGx clinical guidelines and comparing their outputs with expert-annotated evaluations. The two LLMs classified 385 PGx clinical guidelines, with each recommendation tested 20 times per model. Accuracy was assessed by comparing the results with manually labeled data. Two prospectively defined strategies were employed to identify inconsistent predictions. The first involved repeated cross-comparison, flagging discrepancies between the most frequent classifications from each model. The second employed a consistency threshold strategy, which designated predictions appearing in less than 60% of the 40 combined outputs as unstable. Cases flagged by either strategy were subjected to manual review. This study also estimated the overall cost of model usage and was conducted between October 1 and November 30, 2024. Results: GPT-4o and Gemini-1.5-Pro yielded reproducibility rates of 97.8% (7,534/7,700) and 98.9% (7,612/7,700), respectively, based on the most frequent classification for each query. Compared with expert labels, GPT-4o achieved 93.5% accuracy (Cohen’s Kappa=0.90; P<.001) and Gemini-1.5-Pro 92.7% accuracy (Cohen’s Kappa=0.89; P<.001). Both models demonstrated high overall performance, with comparable weighted average F1 scores (GPT-4o: 0.9288; Gemini: 0.9350). The models generated consistent predictions for 341 of 385 guideline items, reducing the need for manual review by 88.6%. Among these agreed-upon cases, only one (0.3%) diverged from expert labels. Applying a predefined agreement-threshold strategy further reduced the number of priority manual review cases to 2.9% (11/385), although the error rate slightly increased to 0.5% (2/374). The inconsistencies identified through these methods prompted prioritization of manual review to minimize errors and enhance clinical applicability. The total combined cost of using both LLMs was only $0.76. Conclusions: These findings suggest that using two LLMs can effectively streamline PGx guideline integration into CDSS, while maintaining high performance and minimal cost. Although selective manual review remains necessary, this approach offers a practical and scalable solution for PGx guideline classification in clinical workflows.

This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 19, 2006, and September 13, 2018, with estimated glomerular filtration rate (eGFR) measurements available within a 7-day interval of the imaging examination. We developed and compared three model configurations: using a single fundus image (Model A), combining a single image with demographic features (Model B), and integrating bilateral fundus images (Model C). We compared two base architectures, EfficientNet-B3 and EfficientNetV2-S, and evaluated the impact of different training strategies: a single model versus a 5-fold cross-validation (CV) ensemble. Model performance was assessed using the Area Under the Curve (AUC), sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV). Among all evaluated models, the bilateral-image model (Model C) utilizing the EfficientNet-B3 architecture with a 5-fold CV ensemble strategy demonstrated the best overall performance, achieving an AUC of 0.868, with a sensitivity of 0.792 and a specificity of 0.788 on an independent test set. The performance of this ensemble strategy was statistically superior to its single-model counterpart trained on the full dataset (AUC 0.850, p < 0.001). Among single models, Model B yielded the highest AUC (0.857) and sensitivity (0.794), while Model C offered the highest specificity (0.799), revealing a clinical trade-off between the different approaches. Furthermore, benchmarking against the newer EfficientNetV2-S architecture did not yield a performance benefit in this study. The study exhibited a superior performance in detecting advanced chronic kidney disease in patients with diabetes mellitus through retinal fundus image.

The peripheral nervous system is the bridge of communication between the central nervous system and other body systems. Autologous nerve grafting is the mainstream method for repair of nerve lesions greater than 20 mm. However, there are several disadvantages and limitations of autologous nerve grafting, thus prompting the need for fabrication of nerve conduits for clinical use. In this study, we successfully fabricated astragaloside (Ast)-containing polyurethane (PU) nerve guidance conduits via digital light processing, and it was noted that the addition of Ast improved the hydrophilicity of traditional PU conduits by at least 23%. The improved hydrophilicity not only led to enhanced cellular proliferation of rat Schwann cells, we also noted that levels of inflammatory markers tumor necrosis factor-alpha (TNF-α) and cyclooxygenase-2 (COX-2) significantly decreased with increasing concentrations of Ast. Furthermore, the levels of neural regeneration markers were significantly enhanced with the addition of Ast. This study demonstrated that Ast-containing PU nerve conduits can be potentially used as an alternative solution to regenerate peripheral nerve injuries.

Background: Although sex differences in the epidemiological features of aortic dissection (AD) are known, whether there were sex differences in the associations of comorbidities and risk factors with AD is unclear. We evaluated the temporal trends and risk factors of AD by sex. Methods and Results: Using claims data from a universal health insurance program linked to the National Death Registry in Taiwan, we identified 16 368 men and 7052 women with newly diagnosed AD from 2005 to 2018. In the case‐control analysis, a matched control group without AD was selected for men and women separately. Conditional logistic regression was used to evaluate risk factors of AD and sex differences. Over the 14 years, the annual incidence of diagnosed AD was 12.69 and 5.34 per 100 000 in men and women, respectively. The 30‐day mortality was greater in women than in men (18.1% versus 14.1%; adjusted odds ratio [95% CI], 1.19 [1.10–1.29]), and the sex difference was observed mainly in patients not treated with surgery. The 30‐day mortality declined over time in male patients undergoing surgical treatments, but no significantly temporal change was found in other patient groups stratified by sex and surgery. After multivariable adjustments, atrial fibrillation, chronic kidney disease, and coronary artery bypass graft surgery were associated with a greater increase in the odds of AD occurrence in women than in men. Conclusions: Greater 30‐day mortality and stronger associations of atrial fibrillation, chronic kidney disease, and coronary artery bypass graft surgery with AD in women than in men require further attention.