MedPodGPT: A multilingual audio-augmented large language model for medical research and education.

The proliferation of medical podcasts has generated an extensive repository of audio content, rich in specialized terminology, diverse medical topics, and expert dialogues. Here we introduce a computational framework designed to enhance large language models (LLMs) by leveraging the informational content of publicly accessible medical podcast data. This dataset, comprising over 4, 300 hours of audio content, was transcribed to generate over 39 million text tokens. Our model, MedPodGPT, integrates the varied di-alogue found in medical podcasts to improve understanding of natural language nuances, cultural contexts, and medical knowledge. Evaluated across multiple benchmarks, MedPodGPT demonstrated an average improvement of 2.31% over standard open-source benchmarks and showcased an improvement of 2.58% in its zero-shot multilingual transfer ability, effectively generalizing to different linguistic contexts. By harnessing the untapped potential of podcast content, MedPodGPT advances natural language processing, offering enhanced capabilities for various applications in medical research and education.

Survival analysis on subchondral bone length for total knee replacement.

OBJECTIVE: Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR). METHODS: We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk. RESULTS: The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827. CONCLUSION: SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

Atypical vocal quality in women with the FMR1 premutation: an indicator of impaired sensorimotor control.

Women with the FMR1 premutation are susceptible to motor involvement related to atypical cerebellar function, including risk for developing fragile X tremor ataxia syndrome. Vocal quality analyses are sensitive to subtle differences in motor skills but have not yet been applied to the FMR1 premutation. This study examined whether women with the FMR1 premutation demonstrate differences in vocal quality, and whether such differences relate to FMR1 genetic, executive, motor, or health features of the FMR1 premutation. Participants included 35 women with the FMR1 premutation and 45 age-matched women without the FMR1 premutation who served as a comparison group. Three sustained /a/ vowels were analyzed for pitch (mean F0), variability of pitch (standard deviation of F0), and overall vocal quality (jitter, shimmer, and harmonics-to-noise ratio). Executive, motor, and health indices were obtained from direct and self-report measures and genetic samples were analyzed for FMR1 CGG repeat length and activation ratio. Women with the FMR1 premutation had a lower pitch, larger pitch variability, and poorer vocal quality than the comparison group. Working memory was related to harmonics-to-noise ratio and shimmer in women with the FMR1 premutation. Vocal quality abnormalities differentiated women with the FMR1 premutation from the comparison group and were evident even in the absence of other clinically evident motor deficits. This study supports vocal quality analyses as a tool that may prove useful in the detection of early signs of motor involvement in this population.