{"id":"W4390498700","doi":"10.1302/1358-992x.2024.1.078","title":"MACHINE LEARNING CAN PREDICT DIFFICULTY IN ANTERIOR APPROACH TOTAL HIP ARTHROPLASTY TO IMPROVE PATIENT SAFETY AND SURGICAL TRAINING","year":2024,"lang":"en","type":"article","venue":"Orthopaedic Proceedings","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Receiver operating characteristic; Confidence interval; Logistic regression; Odds ratio; Radiography; Surgery; Learning curve; Arthroplasty; Internal medicine; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006659661,0.0004853749,0.0007622958,0.0004442714,0.0002338445,0.0001611165,0.00006795604,0.000210361,0.0001082068],"category_scores_gemma":[0.0002460787,0.0003760435,0.0001892908,0.0006434567,0.0001841069,0.0002399385,0.0002296704,0.001054974,0.00001625032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001409611,"about_ca_system_score_gemma":0.0001857138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006715988,"about_ca_topic_score_gemma":0.00001766247,"domain_scores_codex":[0.996546,0.00002753026,0.0007978792,0.001036645,0.0006373452,0.0009546212],"domain_scores_gemma":[0.9989237,0.00007913319,0.0001050949,0.0001218554,0.00008188059,0.000688371],"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":[0.001784504,0.000355256,0.1453195,0.001336795,0.000176772,0.001728671,0.01476786,0.00002551583,0.002788602,0.0005982883,0.0003748669,0.8307434],"study_design_scores_gemma":[0.02212323,0.0125824,0.3248896,0.008782769,0.0006791846,0.04255195,0.01739139,0.1075347,0.0004416892,0.00004613512,0.4594395,0.003537445],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901674,0.001496177,0.000125777,0.0009804204,0.0005611966,0.001013386,0.0001280968,0.0003996022,0.005127974],"genre_scores_gemma":[0.99672,0.000480617,0.0006148022,0.0002032489,0.0005576583,0.00008563976,0.00007671077,0.00008436702,0.001177028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.827206,"threshold_uncertainty_score":0.9998692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009860428899740979,"score_gpt":0.2273143158140842,"score_spread":0.2174538869143433,"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."}}