{"id":"W4327675624","doi":"10.1001/jamanetworkopen.2023.3391","title":"Artificial Intelligence for Hip Fracture Detection and Outcome Prediction","year":2023,"lang":"en","type":"review","venue":"JAMA Network Open","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Sunnybrook Hospital; University of Toronto","funders":"","keywords":"Medicine; Logistic regression; Hip fracture; MEDLINE; Machine learning; Receiver operating characteristic; Cochrane Library; Data extraction; Artificial intelligence; Systematic review; Meta-analysis; Computer science; Internal medicine; Osteoporosis","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":[0.0189611,0.001185894,0.002890371,0.006419879,0.0003582327,0.003424616,0.001528028,0.002135097,0.002477021],"category_scores_gemma":[0.09172025,0.0004970228,0.003231673,0.005094061,0.001345472,0.002152966,0.001329478,0.002143272,0.0004262105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001751505,"about_ca_system_score_gemma":0.003573187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004011814,"about_ca_topic_score_gemma":0.003632847,"domain_scores_codex":[0.9809418,0.01265531,0.002428084,0.0008541384,0.00299482,0.0001259472],"domain_scores_gemma":[0.9053807,0.08433361,0.005810752,0.001119052,0.003091748,0.0002641516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005420094,0.0001657732,0.01479688,0.2365447,0.01246058,0.0002354906,0.0003760947,0.0160972,0.0005623263,0.01146655,0.01566461,0.6910878],"study_design_scores_gemma":[0.001260325,0.00341039,0.05820851,0.4506969,0.03456273,0.002218326,0.001301566,0.07658261,0.003204822,0.2039095,0.1638474,0.0007969039],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003951397,0.9634693,0.017818,0.009143045,0.0008652611,0.0003873058,0.000953377,0.0001121987,0.003300201],"genre_scores_gemma":[0.1617563,0.7712318,0.05711313,0.004511575,0.002639158,0.001192412,0.001026089,0.00003749192,0.0004920661],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.0189611,"threshold_uncertainty_score":0.1002771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4990980623636097,"score_gpt":0.5286265198227584,"score_spread":0.02952845745914878,"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."}}