Sharmila Majumdar

Objective: Nonspecific low back pain (ns-LBP) is a widespread health challenge, and opioids were frequently prescribed upon first LBP presentation in the United States during the opioid epidemic. We assessed temporal changes in quarterly (Q) opioid prescriptions for new-onset ns-LBP from 2012 through 2024 at a California medical center amid evolving voluntary federal recommendations and mandatory state regulations. Methods: We analyzed opioid prescription rates (prescription within 6 months of diagnosis) for ns-LBP patients diagnosed in all departments and stratified by emergency department (ED), and we compared high- versus low-dose prescription (≥50 morphine mg equivalents/day). Change point detection identified transitions without policy priors, and interrupted time series regression quantified level changes and trend modifications. Results: Of 31 940 ns-LBP patients, 2077 (6.5%) received opioids (mean [SD] age, 51.0 [17.6] y; 45.3% male; 53.7% White). Change points occurred at 2015 Q2 and 2018 Q4. Prescribing odds declined 2.7% per quarter from 2012 Q1 through 2024 Q4 (95% CI, 0.4%-5.0%; P = .02). ED patients (16.5% prescribed opioids vs 5.8% non-ED) showed a 54.2% immediate reduction at 2015 Q2 (95% CI, 18.8%-74.2%; P = .008), declining from 27.4% to 12.2% before plateauing; non-ED patients showed a 42.8% reduction at 2018 Q4 (95% CI, 27.5%-54.9%; P < .001). High-dose prescribing remained unchanged among opioid recipients. Conclusion: Change points coincided with anticipatory adoption of the 2016 Centers for Disease Control and Prevention recommendations and 2018 California mandatory drug monitoring implementation. ED settings showed disproportionate reductions at 2015 Q2, consistent with knowledge diffusion of voluntary guidelines in settings with frequent opioid prescribing, while non-ED reductions coincided with immediate regulatory compliance at 2018 Q4. This distinction can guide future policy selection to address environment-specific adoption barriers.

Objectives: This study aimed to validate a segmentation pipeline for the extraction of volumetric thigh muscle data, investigate the influence of demographics on segmentation performance, and investigate the influence of low-quality segmentations on quantitative performance and downstream metrics. Materials and methods: Manual thigh muscle segmentations on axial T1-weighted spin echo thigh images of 364 (106 training and validation; 258 testing) participants of the Osteoarthritis Initiative (OAI) were used. A trained YOLO (You Only Look Once) object detection model generated labels and box predictions, that were passed to a fine-tuned foundation model (Segment Anything, Meta), which generated muscle segmentations. The quality of segmentations was assessed by two radiologists (none, minor, major issue), and Dice scores and mean bias versus manual segmentations were calculated in the testing dataset. Linear regression was used to calculate the associations of age, sex, and BMI with Dice scores. Results: Dice scores ranged between 0.97 (quadriceps) and 0.89 (adductors). Exclusion of erroneous segmentations decreased mean bias by 0.06 mL across muscle groups; mean Dice of segmentations with versus without major issues was 0.90 versus 0.94. Female sex was associated with marginally lower Dice scores (beta -0.01, 95% CI: -0.02, -0.01, p < 0.001), while no other demographic feature showed significant associations. Conclusion: Foundation models can be fine-tuned on a modest task-specific dataset to achieve segmentation accuracy comparable to bespoke networks, without training a dedicated architecture from scratch. Visually erroneous segmentations introduce only negligible degradation of quantitative performance and downstream metrics and may thus be challenging to identify by data-driven detection strategies. Key Points: Question Foundation models can be efficiently leveraged for robust segmentation on complex thigh muscle anatomy Findings Radiologist-identified erroneous segmentations introduced only negligible degradation of summary quantitative performance and are thus difficult to identify using statistical methods. Relevance statement Human-in-the-loop quality assurance post-validation adds limited value to improve downstream volumetric muscle metrics in foundation-model-based thigh segmentation. These findings carry implications for both research and potential clinical applications of muscle volume quantification, e.g ., personalized therapeutic regimens.

Proprietary and open-source large language models (LLMs) generated radiologically relevant indications from clinical notes that were more comprehensive and factual than clinician indications, and when generated by the proprietary LLM, were ranked most useful in protocoling and imaging interpretation by radiologists.

We read with great appreciation the letter by Østergaard et al¹ in response to our recent article.² We commend the authors for their thoughtful engagement with the topic and for expanding on several key points with relevant and up-to-date evidence, particularly regarding the role of conventional and synthetic computed tomography (CT) in assessing structural damage of the joints.