{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01083219,0.001464046,0.00142775,0.001069338,0.0003533459,0.0007635699,0.0012403,0.0008438616,0.001402785],"category_scores_gemma":[0.01254579,0.0004052833,0.001525519,0.0006519759,0.0003388699,0.0006441419,0.0009525166,0.001136094,0.0002759994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295775,"about_ca_system_score_gemma":0.002939377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01792184,"about_ca_topic_score_gemma":0.008542259,"domain_scores_codex":[0.9985903,0.0009254903,0.00008792346,0.0001633584,0.0001151835,0.0001176121],"domain_scores_gemma":[0.9892619,0.007976952,0.0004148878,0.0004003437,0.001770657,0.0001752879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003213045,0.003029797,0.1273267,0.0005700194,0.001060102,0.0001987927,0.0001712813,0.7342841,0.001835207,0.0006110691,0.002473813,0.1252261],"study_design_scores_gemma":[0.0001134805,0.0009149413,0.005530399,0.00002202134,0.0001162476,0.00003036303,0.0000197322,0.9924788,0.0004663956,0.0001262125,0.0001706256,0.00001057576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9086476,0.0009172297,0.0864059,0.0002929946,0.00006550774,0.001075431,0.0007694148,0.0007453783,0.001080578],"genre_scores_gemma":[0.9535822,0.0002730197,0.04337199,0.00008801412,0.00001962112,0.0008534218,0.001218966,0.00002932351,0.0005634663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01792184,"threshold_uncertainty_score":0.0572868,"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."}}