{"id":"W4413745927","doi":"10.2196/75760","title":"A Machine Learning Model for Predicting Sarcopenia Among Middle-Aged Adults: Development and External Validation","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sarcopenia; Computer science; Medicine; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02190513,0.001702233,0.00143318,0.002274373,0.0007309648,0.001169399,0.001921422,0.001684328,0.001432123],"category_scores_gemma":[0.02841524,0.0004817482,0.001740569,0.001224766,0.0006914994,0.001109538,0.001618097,0.00178345,0.0007648209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009611908,"about_ca_system_score_gemma":0.00241241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01072777,"about_ca_topic_score_gemma":0.005020571,"domain_scores_codex":[0.9957259,0.002548099,0.0003847784,0.0007174063,0.0003936936,0.0002300566],"domain_scores_gemma":[0.9819872,0.0123366,0.0007592316,0.00109859,0.003472223,0.0003461156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002044916,0.002763192,0.3942004,0.0003959194,0.00126209,0.000356808,0.0003599271,0.3943871,0.001944747,0.0009240893,0.006877366,0.1944834],"study_design_scores_gemma":[0.0001208122,0.0004048488,0.0208113,0.00006666977,0.0001012082,0.00005114211,0.0000655976,0.9768836,0.0005203103,0.0004532702,0.0005012982,0.0000199676],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8405024,0.001336002,0.1503441,0.0005231352,0.0002404455,0.001408756,0.002782878,0.001207642,0.001654517],"genre_scores_gemma":[0.9242496,0.0002905401,0.06882246,0.000172929,0.00005552299,0.001356112,0.004393705,0.00004955944,0.0006095633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02190513,"threshold_uncertainty_score":0.1158468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.037017534500721,"score_gpt":0.3339868702412107,"score_spread":0.2969693357404897,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}