{"id":"W4388870160","doi":"10.2196/46791","title":"A Machine Learning–Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study","year":2023,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hong Kong Polytechnic University","keywords":"Osteoporosis; Machine learning; Logistic regression; Medicine; Receiver operating characteristic; Artificial intelligence; Naive Bayes classifier; Gradient boosting; Predictive modelling; Support vector machine; Physical therapy; Computer science; Random forest; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505234,0.0001660165,0.0003117802,0.0003047191,0.0003505377,0.00005992753,0.00007083216,0.00008362295,0.00005896839],"category_scores_gemma":[0.0003674202,0.000148746,0.00005023217,0.0004494826,0.00003401539,0.0001037239,0.000140656,0.0005191113,0.0000737904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000062499,"about_ca_system_score_gemma":0.0003006383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005415127,"about_ca_topic_score_gemma":0.00001898147,"domain_scores_codex":[0.9978814,0.000134081,0.0004788868,0.0004344507,0.0006025669,0.0004686114],"domain_scores_gemma":[0.9990519,0.0002352012,0.00008600949,0.0002199778,0.000141481,0.0002653906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006291736,0.0004790643,0.8358103,0.0005483133,0.00005475303,0.00007766078,0.005458935,0.002492437,0.00119433,0.000003941542,0.0003289548,0.1529221],"study_design_scores_gemma":[0.004801455,0.0007751042,0.545525,0.0002563585,0.00004479474,0.000009283559,0.001476106,0.4437047,0.001317214,0.000006723844,0.001835407,0.0002478012],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956033,0.00007982348,0.0007296572,0.001666889,0.00002953209,0.00143015,0.000003646785,0.0002979829,0.0001590427],"genre_scores_gemma":[0.9932914,0.0000143875,0.00401363,0.0004452709,0.0000520736,0.000318605,0.0001634223,0.00003977653,0.001661428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4412123,"threshold_uncertainty_score":0.6065686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09416044655924724,"score_gpt":0.4027873650754462,"score_spread":0.3086269185161989,"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."}}