{"id":"W3045092065","doi":"10.1080/10255842.2020.1793331","title":"Predicting population level hip fracture risk: a novel hierarchical model incorporating probabilistic approaches and factor of risk principles","year":2020,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics & Biomedical Engineering","topic":"Hip and Femur Fractures","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hip fracture; Population; Probabilistic logic; Risk factor; Psychological intervention; Risk assessment; Scale (ratio); Fracture (geology); Medicine; Risk analysis (engineering); Computer science; Physical therapy; Osteoporosis; Engineering; Environmental health; Artificial intelligence; Geography; Internal medicine; Computer security; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001098616,0.0003371842,0.0007153827,0.0003646075,0.00006353462,0.00002714223,0.0001900215,0.0004123079,0.000004170222],"category_scores_gemma":[0.002431063,0.0002841383,0.0001125206,0.0006731154,0.0000839229,0.00009268992,0.0002791203,0.001071656,2.952007e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007385325,"about_ca_system_score_gemma":0.00007761658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004898701,"about_ca_topic_score_gemma":0.000001611254,"domain_scores_codex":[0.9976398,0.0001501894,0.0008394977,0.0005924078,0.000423979,0.0003541436],"domain_scores_gemma":[0.9981782,0.0007504909,0.0003259944,0.0002542306,0.00005044706,0.0004406312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004368516,0.0009690825,0.02273707,0.004431503,0.00047756,0.0000492319,0.01465611,0.1559301,0.2010592,0.005579076,0.00001259503,0.5936615],"study_design_scores_gemma":[0.001015522,0.0002087374,0.01028423,0.000252775,0.00007916449,0.00002277875,0.00003633988,0.9853767,0.001436668,0.0009691131,0.00007201498,0.0002459277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2206763,0.0001423436,0.7782122,0.0002586849,0.0001289654,0.0003647017,0.00009607338,0.0001184894,0.000002251056],"genre_scores_gemma":[0.5046266,0.00001227436,0.4950179,0.00006420969,0.0001996696,0.000009636278,0.00004086127,0.0000284286,3.902788e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8294466,"threshold_uncertainty_score":0.9999611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022528331218726,"score_gpt":0.3152536885569083,"score_spread":0.2130008554350357,"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."}}