Health Care Burden Associated With Adolescent Prolonged Opioid Use After Surgery.

BACKGROUND: Prolonged opioid use after surgery (POUS), defined as the filling of at least 1 opioid prescription filled between 90 and 180 days after surgery, has been shown to increase health care costs and utilization in adult populations. However, its economic burden has not been studied in adolescent patients. We hypothesized that adolescents with POUS would have higher health care costs and utilization than non-POUS patients. METHODS: Opioid-naive patients 12 to 21 years of age in the United States who received outpatient prescription opioids after surgery were identified from insurance claim data from the Optum Clinformatics Data Mart Database from January 1, 2003, to June 30, 2019. The primary outcomes were total health care costs and visits in the 730-day period after the surgical encounter in patients with POUS versus those without POUS. Multivariable regression analyses were used to determine adjusted health care cost and visit differences. RESULTS: A total of 126,338 unique patients undergoing 132,107 procedures were included in the analysis, with 4867 patients meeting criteria for POUS for an incidence of 3.9%. Adjusted mean total health care costs in the 730 days after surgery were $4604 (95% confidence interval [CI], $4027-$5181) higher in patients with POUS than that in non-POUS patients. Patients with POUS had increases in mean adjusted inpatient length of stay (0.26 greater [95% CI, 0.22-0.30]), inpatient visits (0.07 greater [95% CI, 0.07-0.08]), emergency visits (0.96 greater [95% CI, 0.89-1.03]), and outpatient/other visits (5.78 greater [95% CI, 5.37-6.19]) in the 730 days after surgery ( P < .001 for all comparisons). CONCLUSIONS: In adolescents, POUS was associated with increased total health care costs and utilization in the 730 days after their surgical encounter. Given the increased health care burden associated with POUS in adolescents, further investigation of preventative measures for high-risk individuals and additional study of the relationship between opioid prescription and outcomes may be warranted.

Automated AJCC Restaging for Oropharyngeal Cancer Research.

With the American Joint Committee on Cancer (AJCC) 8th edition staging guidelines update, human papillomavirus-positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) is now staged separately from its HPV-negative counterpart, preventing meaningful comparison of cases staged with the 7th versus 8th edition criteria. Manual restaging is time-consuming and error-prone, hindering multiyear analyses for HPV+ OPSCC. We developed an automated computational tool for re-classifying HPV+ OPSCC pathological and clinical tumor staging from AJCC 7th to 8th edition. The tool is designed to handle large data sets, ensuring comprehensive and accurate analysis of historic HPV+ OPSCC data. Validated against institutional and National Cancer Database data sets, the algorithm achieved accuracies of 100% (95% confidence interval [CI] 98.8%-100%) and 93.4% (95% CI 93.1%-93.7%), successfully restaging 326/326 and 26,505/28,374 cases, respectively. With its open-source design, this computational tool can enhance future HPV+ OPSCC research and inspire similar tools for other cancer types and subsequent AJCC editions.

Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment.

[This corrects the article DOI: 10.2196/45312.].

The Impact of Four Smoking Metrics on Survival After Diagnosis with HPV+ Oropharyngeal Cancer.

OBJECTIVE: While tobacco use is understood to negatively impact HPV+ oropharyngeal squamous cell carcinoma (OPSCC) outcomes, debate remains as to how this impact differs between cohorts. Multiple smoking metrics have been identified as having the greatest prognostic significance, and some recent works have found smoking to have no significant impact. Herein, we show through an analysis of four common smoking metrics that while smoking impacts overall survival (OS), it has a limited impact on recurrence-free survival (RFS) in our cohort. METHODS: We conducted a retrospective review of patients treated for HPV+ OPSCC in our health system from 2012 to 2019. Patients with metastatic disease or concurrent second primaries were excluded. Four metrics of tobacco use were assessed: current/former/never smokers, ever/never smokers, and smokers with >10 or >20 pack-year (PY) smoking histories. Our main outcomes were 3-year RFS and OS. RESULTS: Three hundred and sixty-seven patients met inclusion criteria. 37.3% of patients (137/367) were never-smokers; 13.8% of patients (51/367) were currently smoking at diagnosis and 48.8% of patients (179/367) were former smokers. No tobacco-use metric significantly impacted 3-year RFS. On univariate analysis, all smoking metrics yielded inferior OS. On multivariate analysis, current and ever smoking status significantly impacted 3-year OS. CONCLUSION: The impact of tobacco use on HPV+ OPSCC outcomes is not universal, but may instead be modulated by other cohort-specific factors. The impact of smoking may decrease as rates of tobacco use decline. LEVEL OF EVIDENCE: 3 (Cohort and case-control studies) Laryngoscope, 134:3158-3164, 2024.

ChatGPT for Automated Cross-Checking of Authors' Conflicts of Interest Against Industry Payments.

OBJECTIVE: The Centers for Medicare & Medicaid Services "OpenPayments" database tracks industry payments to US physicians to improve research conflicts of interest (COIs) transparency, but manual cross-checking of articles' authors against this database is labor-intensive. This study aims to assess the potential of large language models (LLMs) like ChatGPT to automate COI data analysis in medical publications. STUDY DESIGN: An observational study analyzing the accuracy of ChatGPT in automating the cross-checking of COI disclosures in medical research articles against the OpenPayments database. SETTING: Publications regarding Food and Drug Administration-approved biologics for chronic rhinosinusitis with nasal polyposis: omalizumab, mepolizumab, and dupilumab. METHODS: First, ChatGPT evaluated author affiliations from PubMed to identify those based in the United States. Second, for author names matching 1 or multiple payment recipients in OpenPayments, ChatGPT undertook a comparative analysis between author affiliation and OpenPayments recipient metadata. Third, ChatGPT scrutinized full article COI statements, producing an intricate matrix of disclosures for each author against each relevant company (Sanofi, Regeneron, Genentech, Novartis, and GlaxoSmithKline). A random subset of responses was manually checked for accuracy. RESULTS: In total, 78 relevant articles and 294 unique US authors were included, leading to 980 LLM queries. Manual verification showed accuracies of 100% (200/200; 95% confidence interval [CI]: 98.1%-100%) for country analysis, 97.4% (113/116; 95% CI: 92.7%-99.1%) for matching author affiliations with OpenPayments metadata, and 99.2% (1091/1100; 95% CI: 98.5%-99.6%) for COI statement data extraction. CONCLUSION: LLMs have robust potential to automate author-company-specific COI cross-checking against the OpenPayments database. Our findings pave the way for streamlined, efficient, and accurate COI assessment that could be widely employed across medical research.

Automated HEART score determination via ChatGPT: Honing a framework for iterative prompt development.

Objectives: This study presents a design framework to enhance the accuracy by which large language models (LLMs), like ChatGPT can extract insights from clinical notes. We highlight this framework via prompt refinement for the automated determination of HEART (History, ECG, Age, Risk factors, Troponin risk algorithm) scores in chest pain evaluation. Methods: We developed a pipeline for LLM prompt testing, employing stochastic repeat testing and quantifying response errors relative to physician assessment. We evaluated the pipeline for automated HEART score determination across a limited set of 24 synthetic clinical notes representing four simulated patients. To assess whether iterative prompt design could improve the LLMs' ability to extract complex clinical concepts and apply rule-based logic to translate them to HEART subscores, we monitored diagnostic performance during prompt iteration. Results: Validation included three iterative rounds of prompt improvement for three HEART subscores with 25 repeat trials totaling 1200 queries each for GPT-3.5 and GPT-4. For both LLM models, from initial to final prompt design, there was a decrease in the rate of responses with erroneous, non-numerical subscore answers. Accuracy of numerical responses for HEART subscores (discrete 0-2 point scale) improved for GPT-4 from the initial to final prompt iteration, decreasing from a mean error of 0.16-0.10 (95% confidence interval: 0.07-0.14) points. Conclusion: We established a framework for iterative prompt design in the clinical space. Although the results indicate potential for integrating LLMs in structured clinical note analysis, translation to real, large-scale clinical data with appropriate data privacy safeguards is needed.

Identifying incarceration status in the electronic health record using large language models in emergency department settings.

Background: Incarceration is a significant social determinant of health, contributing to high morbidity, mortality, and racialized health inequities. However, incarceration status is largely invisible to health services research due to inadequate clinical electronic health record (EHR) capture. This study aims to develop, train, and validate natural language processing (NLP) techniques to more effectively identify incarceration status in the EHR. Methods: The study population consisted of adult patients (≥ 18 y.o.) who presented to the emergency department between June 2013 and August 2021. The EHR database was filtered for notes for specific incarceration-related terms, and then a random selection of 1,000 notes was annotated for incarceration and further stratified into specific statuses of prior history, recent, and current incarceration. For NLP model development, 80% of the notes were used to train the Longformer-based and RoBERTa algorithms. The remaining 20% of the notes underwent analysis with GPT-4. Results: There were 849 unique patients across 989 visits in the 1000 annotated notes. Manual annotation revealed that 559 of 1000 notes (55.9%) contained evidence of incarceration history. ICD-10 code (sensitivity: 4.8%, specificity: 99.1%, F1-score: 0.09) demonstrated inferior performance to RoBERTa NLP (sensitivity: 78.6%, specificity: 73.3%, F1-score: 0.79), Longformer NLP (sensitivity: 94.6%, specificity: 87.5%, F1-score: 0.93), and GPT-4 (sensitivity: 100%, specificity: 61.1%, F1-score: 0.86). Conclusions: Our advanced NLP models demonstrate a high degree of accuracy in identifying incarceration status from clinical notes. Further research is needed to explore their scaled implementation in population health initiatives and assess their potential to mitigate health disparities through tailored system interventions.

The Role of Large Language Models in Medical Education: Applications and Implications.

Large language models (LLMs) such as ChatGPT have sparked extensive discourse within the medical education community, spurring both excitement and apprehension. Written from the perspective of medical students, this editorial offers insights gleaned through immersive interactions with ChatGPT, contextualized by ongoing research into the imminent role of LLMs in health care. Three distinct positive use cases for ChatGPT were identified: facilitating differential diagnosis brainstorming, providing interactive practice cases, and aiding in multiple-choice question review. These use cases can effectively help students learn foundational medical knowledge during the preclinical curriculum while reinforcing the learning of core Entrustable Professional Activities. Simultaneously, we highlight key limitations of LLMs in medical education, including their insufficient ability to teach the integration of contextual and external information, comprehend sensory and nonverbal cues, cultivate rapport and interpersonal interaction, and align with overarching medical education and patient care goals. Through interacting with LLMs to augment learning during medical school, students can gain an understanding of their strengths and weaknesses. This understanding will be pivotal as we navigate a health care landscape increasingly intertwined with LLMs and artificial intelligence.

How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment.

BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. OBJECTIVE: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. METHODS: We used 2 sets of multiple-choice questions to evaluate ChatGPT's performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT's performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. RESULTS: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT's answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001) and NBME-Free-Step2 (P=.001) data sets, respectively. CONCLUSIONS: ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT's capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning.

A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2.

PURPOSE: No method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers. METHOD: To assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load). RESULTS: Simulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A vs. viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A vs. viral variant A with masks) given partial data aggregated across multiple cities. CONCLUSIONS: This novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with nonpharmaceutical interventions and emergent viral strains.

Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard.

BACKGROUND: Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. OBJECTIVES: This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. METHODS: We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. RESULTS: The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. CONCLUSION: A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.

Linked Double-Row Equivalent Arthroscopic Rotator Cuff Repair Leads to Significantly Improved Patient Outcomes.

BACKGROUND: Biomechanical studies have demonstrated that arthroscopic rotator cuff repair using a linked double-row equivalent construct results in significantly higher load to failure compared with conventional transosseous-equivalent constructs. PURPOSE: To determine the patient-reported outcomes (PROs), reoperation rates, and complication rates after linked double-row equivalent rotator cuff repair for full-thickness rotator cuff tears. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Consecutive patients who underwent linked double-row equivalent arthroscopic rotator cuff repair with minimum 2-year follow-up were included. The primary outcome was the American Shoulder and Elbow Surgeons (ASES) score at final follow-up. Secondary outcomes included the Simple Shoulder Test (SST), shortened Disabilities of the Arm, Shoulder and Hand (QuickDASH) questionnaire, visual analog scale (VAS), reoperations, and complications. Clinical relevance was defined by the minimally clinically important difference (MCID). Comparisons on an individual level that exceeded MCID (individual-level scores) were deemed clinically relevant. Comparisons between preoperative and postoperative scores were completed using the Student t test. All P values were reported with significance set at P < .05. RESULTS: A total of 42 shoulders in 41 consecutive patients were included in this study (21 male patients [51.2%]; mean age, 64.5 ± 11.9 years; mean follow-up, 29.7 ± 4.5 months). All patients (100%) completed the minimum 2-year follow-up. The rotator cuff tear measured on average 15.2 ± 8.9 mm in the coronal plane and 14.6 ± 9.8 mm in the sagittal plane. The ASES score improved significantly from 35.5 ± 18.2 preoperatively to 93.4 ± 10.6 postoperatively (P < .001). The QuickDASH (P < .001), SST (P < .001), and VAS (P < .001) scores also significantly improved after surgery. All patients (42/42 shoulders; 100%) achieved clinically relevant improvement (met or exceeded MCID) on ASES and SST scores postoperatively. There were no postoperative complications (0.0%) or reoperations (0.0%) at final follow-up. CONCLUSION: Arthroscopic repair of full-thickness rotator cuff tears with the linked double-row equivalent construct results in statistically significant and clinically relevant improvements in PRO scores with low complication rates (0.0%) and reoperation rates (0.0%) at short-term follow-up.

Injectable hyaluronic acid based microrods provide local micromechanical and biochemical cues to attenuate cardiac fibrosis after myocardial infarction.

Repairing cardiac tissue after myocardial infarction (MI) is one of the most challenging goals in tissue engineering. Following ischemic injury, significant matrix remodeling and the formation of avascular scar tissue significantly impairs cell engraftment and survival in the damaged myocardium. This limits the efficacy of cell replacement therapies, demanding strategies that reduce pathological scarring to create a suitable microenvironment for healthy tissue regeneration. Here, we demonstrate the successful fabrication of discrete hyaluronic acid (HA)-based microrods to provide local biochemical and biomechanical signals to reprogram cells and attenuate cardiac fibrosis. HA microrods were produced in a range of physiological stiffness and shown to degrade in the presence of hyaluronidase. Additionally, we show that fibroblasts interact with these microrods in vitro, leading to significant changes in proliferation, collagen expression and other markers of a myofibroblast phenotype. When injected into the myocardium of an adult rat MI model, HA microrods prevented left ventricular wall thinning and improved cardiac function at 6 weeks post infarct.