{"id":"W4403278588","doi":"10.1109/mcna63144.2024.10703888","title":"Exploiting Machine Learning for Osteoporosis Risk Prediction and Early Intervention","year":2024,"lang":"en","type":"article","venue":"","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Machine learning; Osteoporosis; Intervention (counseling); Artificial intelligence; Medicine; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031934,0.0005469689,0.0008733722,0.00154301,0.0003279121,0.0007498725,0.0005446022,0.000817269,0.0007726162],"category_scores_gemma":[0.01192446,0.0002490748,0.0005887981,0.0008311875,0.0002791444,0.0007184809,0.0007895626,0.0009382411,0.0002393023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004545893,"about_ca_system_score_gemma":0.001200867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005044077,"about_ca_topic_score_gemma":0.006885776,"domain_scores_codex":[0.9987066,0.0007282174,0.00007390263,0.0001463007,0.0002505382,0.0000945108],"domain_scores_gemma":[0.9945509,0.004237316,0.0004937281,0.0002459312,0.0003670401,0.000105078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000655165,0.0007044477,0.2141219,0.0002262534,0.000389909,0.0002789448,0.0001221497,0.3381003,0.002090002,0.003852972,0.004204141,0.4352538],"study_design_scores_gemma":[0.00002592845,0.0001817228,0.01110106,0.00006711725,0.00007829266,0.0001196639,0.00005210141,0.9771364,0.001383108,0.008352961,0.001477404,0.00002407494],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5549476,0.01136787,0.412536,0.01090726,0.0003192053,0.0002118237,0.001741945,0.0009794089,0.006988944],"genre_scores_gemma":[0.9641333,0.0009983955,0.03334099,0.0003645119,0.0001298381,0.00004388021,0.0005443094,0.00001147885,0.0004333271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005044077,"threshold_uncertainty_score":0.0168885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817177887805515,"score_gpt":0.3324248519811555,"score_spread":0.3042530731031003,"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."}}