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Background The Supreme Court ruling in Dobbs v Jackson Women’s Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. Objective This study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information. Methods We scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest. Results Our study revealed (1) a transient increase in the number of daily users by 576.86% (95% CI 545.34%-607.92%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI −22.98% to −16.59%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information–related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information–related terms and concomitant negative sentiment in the postdecision leak period. Conclusions The study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information–related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services.

BACKGROUND The supreme court ruling in Dobbs v Jackson Women’s Health Organization overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and creation of new ones that ban or restrict abortion. In addition to denying patients to the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. OBJECTIVE To study the effect of recent abortion legislation on twitter user engagement, sentiment, and expressions of trust in clinicians and privacy of health information. METHODS We scraped tweets containing keywords of interest between Jan 1st 2020 – Oct 17th, 2022, to capture tweets posted before and after the supreme court decision. We the trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using a RoBERTa model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used TFIDF to measure to volume of tweets before and after the decision with respect to negative and positive sentiment that map to our terms of interest. RESULTS Our study revealed 1) a transient increase in the number of daily users by 576.86% (95% CI: 545.34% - 607.92%), p < 0.001, tweeting about abortion, healthcare, and privacy of health information post decision; 2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI: 5-22.98%, -16.59%, p , 0.001 post decision ; 3) a decrease in the association of the latent dimension of trust across most clinician related and health information related terms of interest; 4) an increased frequency of tweets with these clinician related and health information related terms and concomitant negative sentiment in the post decision period. CONCLUSIONS The study suggests that the Dobbs ruling has consequences for health systems and reproductive healthcare that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in expression of trust in clinicians and health information-related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services. CLINICALTRIAL N/A

We aimed to study the simplification of biomedical text via large language models (LLMs). Specifically, we finetuned three language models to perform substitutions of complex words and word phrases for their respective hypernym in biomedical definitions. This process was then evaluated by readability metrics, and two measures of sentence complexity: the measure of lexical diversity (MLTD), and mean dependency distance (MDD) scoring. A sample of 1,000 biomedical definitions in the National Library of Medicine’s Unified Medical Language System (UMLS) was processed with three approaches, each with a different language models and analysis revealed an increase in FK score and a reduction in reading grade level across all metrics. Reading scores improved from a pre-processed collegiate reading level to a post-processed US high-school level. An inter-approach comparison showed that our GPT-J-6b approach had the best improvement in MLTD and MDD. This study demonstrates the merit of hypernym substitution in improving the readability and improving measures cognitive burden of biomedical content for the general public.

Introduction The Military Match is the residency matching system for medical students attending the Uniformed Services University of Health Sciences, and the students were funded by the Health Professions Scholarship Program through the U.S. Army, Air Force, and Navy. To evaluate and compare military residency programs, students use residency program websites. Often, the residency program’s website serves as a key source, or the only point of reference, when considering residency options, especially during times when face-to-face interactions are limited. This report aims to provide a systematic evaluation of military residency programs and their websites. Materials and Methods Utilizing a previously published website usability scoring system, military residency programs were categorized to objectively and quantitatively analyze their websites. Usability was divided into four categories for quantifiable analysis: accessibility, marketing, content quality, and technology. The methodology for this analysis was replicated from published reports that have examined healthcare website usability. Each website was analyzed and scored in four categories: accessibility, content quality, marketing, and technology. A “General Usability” score was calculated for each website using a composite of the key factors within the four categories. An overall score was generated utilizing the weighted percentage across all four categories. To address deficiencies of the original methodology, a secondary analysis was performed on the listed websites utilizing an automated methodology for website usability. Results A comprehensive list of 125 Accreditation Council for Graduate Medical Education U.S. Military residency program websites was compiled. Of these, 96 programs and 106 websites were evaluated. The primary analysis employing usability methodology identified technology as the highest ranked category with a score of 0.749 (SD ± 0.039) (SE 0.005) (P &lt; .05). Marketing and content quality were the lowest scoring categories with mean scores of 0.414 (SD ± 0.054) (SE 0.006) and 0.428 (SD ± 0.229) (SE 0.027), respectively (P &lt; .05). There was no significant difference in overall usability rankings or scores among the 96 residency program websites across the three branches (P &lt; .05). Secondary analysis with the new usability methodology demonstrated military residency websites to exhibit more external backlinking compared to internal backlinking (P &lt; 0.05) and no social media backlinking to any of the 106 analyzed websites. When comparing the three services, the Army had significantly lower external backlinking ranking 43.4 (P &lt; .05) and overall backlinking ranking 56.4 (P &lt; 0.05) when compared to the Navy (mean 48.8 and 71.7, and 43.4). There were no other differences in backlinking rankings across the three branches. Conclusions Residency websites have become a primary way to communicate information to applicants. By assessing the overall usability of the various military residency websites, we determined the effectiveness of these websites to relay information to prospective students interested in applying for military residency. We predict that by improving website accessibility, residency programs increase their effectiveness at communicating information to potential applicants and increase interest in military residency programs.