{"id":"W4417347332","doi":"10.1093/jbmrpl/ziaf191","title":"Evaluating an Artificial Intelligence software for opportunistic low bone mineral density and osteoporosis screening: a validation study","year":2025,"lang":"en","type":"article","venue":"JBMR Plus","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Osteoporosis; Bone mineral; Cohort; Gold standard (test); Retrospective cohort study; Receiver operating characteristic; Standard score","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":[0.01043085,0.0007598404,0.0004417548,0.001184385,0.0002941026,0.0006039554,0.0007674108,0.0006543682,0.0007888253],"category_scores_gemma":[0.02569963,0.0002326627,0.0008364047,0.0004924269,0.0007602416,0.0004615713,0.0008404864,0.000515908,0.0004465422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000499441,"about_ca_system_score_gemma":0.0007785751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002590983,"about_ca_topic_score_gemma":0.001544923,"domain_scores_codex":[0.9948264,0.003650971,0.0003189181,0.0003180485,0.0007183579,0.0001674215],"domain_scores_gemma":[0.9720587,0.01888348,0.001618768,0.002289827,0.004546285,0.0006028587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005717942,0.007215406,0.8564922,0.0004123609,0.001036977,0.0005010837,0.001227738,0.02258512,0.00796751,0.0005791301,0.001762281,0.09450212],"study_design_scores_gemma":[0.001646527,0.03613565,0.6443451,0.0001743971,0.0007761273,0.001447952,0.0008994447,0.2961369,0.01395721,0.0006447549,0.003738184,0.00009768282],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942098,0.00008530835,0.004268067,0.00004022015,0.00001160959,0.0004360086,0.0002694335,0.000101987,0.0005776066],"genre_scores_gemma":[0.9906681,0.00006759584,0.007477034,0.00005318414,0.00001389681,0.0003423652,0.0009858717,0.00001926716,0.000372591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01043085,"threshold_uncertainty_score":0.05516428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1691137248673079,"score_gpt":0.4522383839189086,"score_spread":0.2831246590516007,"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."}}