{"id":"W4292363604","doi":"10.2196/38226","title":"The Application of Machine Learning in Predicting Mortality Risk in Patients With Severe Femoral Neck Fractures: Prediction Model Development Study","year":2022,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"Hip and Femur Fractures","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Receiver operating characteristic; Intensive care unit; Femoral neck; Logistic regression; Hip fracture; Random forest; Intensive care; Machine learning; Emergency medicine; Artificial intelligence; Algorithm; Intensive care medicine; Internal medicine; Computer science; Osteoporosis","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":[0.0003562556,0.0001070106,0.0001926034,0.0001931994,0.0002222006,0.000007671953,0.0000882204,0.0001087073,0.000001859504],"category_scores_gemma":[0.00003956726,0.00006803688,0.00001349182,0.0002888863,0.00005791598,0.00005331483,0.0001332172,0.0006796157,2.419406e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007251905,"about_ca_system_score_gemma":0.00005807283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009327072,"about_ca_topic_score_gemma":0.0003080707,"domain_scores_codex":[0.9990084,0.00002862299,0.000441116,0.0001228544,0.0002415042,0.0001575078],"domain_scores_gemma":[0.9994719,0.00002479081,0.000268915,0.0001823651,0.00002808588,0.00002395714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002283059,0.0003103601,0.9580803,0.00003790322,0.00002843273,7.016126e-7,0.00298987,0.004285274,0.000005449658,0.00001311897,0.000003614543,0.03401665],"study_design_scores_gemma":[0.001708865,0.000633157,0.7206981,0.00001259843,0.00001459129,0.000002877804,0.002345494,0.2742237,0.00003919742,0.00002611881,0.0002405265,0.00005483854],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973852,0.00003137616,0.001197932,0.0001051162,0.00001357149,0.001146971,0.00002330484,0.00004560858,0.00005092402],"genre_scores_gemma":[0.9983549,0.00002100275,0.001362582,0.00003266673,0.000003711313,0.0001347889,0.00007664199,0.000006929385,0.000006845842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2699384,"threshold_uncertainty_score":0.295263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006685007403156387,"score_gpt":0.2469876563850706,"score_spread":0.2403026489819142,"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."}}