{"id":"W4281391872","doi":"10.1007/s10140-022-02060-2","title":"Detecting total hip arthroplasty dislocations using deep learning: clinical and Internet validation","year":2022,"lang":"en","type":"article","venue":"Emergency Radiology","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Generalizability theory; Medicine; Triage; Convolutional neural network; Radiography; Arthroplasty; Total hip arthroplasty; Dislocation; Artificial intelligence; Radiology; Computer science; Medical emergency; Surgery; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004870568,0.0001217478,0.0002590469,0.00009364195,0.0003743919,0.00000480593,0.00003675469,0.00006742451,0.002584147],"category_scores_gemma":[0.000320782,0.0001159414,0.00008604604,0.0001432716,0.0001232661,0.00005402621,0.0001224445,0.0005114828,0.00001586213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003024591,"about_ca_system_score_gemma":0.00004794662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003499573,"about_ca_topic_score_gemma":0.00001100449,"domain_scores_codex":[0.9984818,0.0003432151,0.0004742536,0.0003256247,0.0001231814,0.0002519414],"domain_scores_gemma":[0.9994996,0.00008805825,0.0001409663,0.0001304817,0.00002604037,0.0001148805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000112268,0.0001082059,0.9256877,0.00001479923,0.00009835631,0.00003968952,0.0006357611,0.0004251364,0.0007908919,0.0001890312,0.0005279284,0.0713702],"study_design_scores_gemma":[0.003526475,0.003279373,0.8117431,0.00002404842,0.000349655,0.01353185,0.002710923,0.09789002,0.0001360303,0.0001964582,0.06601872,0.0005933003],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920257,0.0005212484,0.004370936,0.0002086644,0.002274288,0.0001817554,0.000004469758,0.00006088248,0.0003520109],"genre_scores_gemma":[0.9978234,0.0001863432,0.0005000224,0.00005554558,0.0004508492,0.00001867223,0.00005268801,0.00001965799,0.0008928542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1139446,"threshold_uncertainty_score":0.9983276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04153010952722095,"score_gpt":0.327193376905275,"score_spread":0.285663267378054,"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."}}