{"id":"W7118072839","doi":"10.1093/geroni/igaf122.3621","title":"Machine Learning Prediction for Functional Impairment, Falls, and Fractures in Postmenopausal Women","year":2025,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Random forest; Brier score; Postmenopausal women; Robustness (evolution); Predictive modelling; Feature (linguistics); Feature engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001021873,0.00009837431,0.0001141407,0.000930634,0.0001271168,0.00008694441,0.0001299895,0.00005920068,0.000007697595],"category_scores_gemma":[0.000303186,0.0001047249,0.000007169536,0.001469321,0.00001406714,0.0003180278,0.0001019911,0.0003803099,6.770302e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002477212,"about_ca_system_score_gemma":0.00008975685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001571455,"about_ca_topic_score_gemma":0.00003365822,"domain_scores_codex":[0.9988376,0.00008401377,0.0004026583,0.0003065587,0.0001339667,0.0002352036],"domain_scores_gemma":[0.9994468,0.000193334,0.0001156717,0.0001220094,0.0001046509,0.00001751227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003036411,0.00003842967,0.8851849,0.0001105108,0.000007188721,0.000001968832,0.002273553,0.01226796,0.0005691813,0.05905443,0.000069245,0.04039223],"study_design_scores_gemma":[0.0008441026,0.0001113937,0.7137378,0.0000931465,7.644688e-7,0.000003705541,0.0001600272,0.2717903,0.00005177841,0.009028242,0.004087179,0.00009153433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7954418,0.0001559243,0.1991839,0.003969294,0.0003972262,0.0003284484,0.000003282451,0.0001342188,0.0003859176],"genre_scores_gemma":[0.9942334,0.000007409356,0.004256977,0.001102126,0.00004650669,0.0001283174,0.00003793614,0.00000641505,0.0001809073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2595223,"threshold_uncertainty_score":0.4270556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254575394376737,"score_gpt":0.2977197083411625,"score_spread":0.2851739543973951,"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."}}