{"id":"W3197829494","doi":"10.1186/s42836-021-00087-3","title":"Development and internal validation of machine learning algorithms to predict patient satisfaction after total hip arthroplasty","year":2021,"lang":"en","type":"article","venue":"Arthroplasty","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"WOMAC; Medicine; Physical therapy; Algorithm; Patient satisfaction; Discriminative model; Machine learning; Logistic regression; Arthroplasty; Osteoarthritis; Artificial intelligence; Surgery; Computer science; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001450473,0.0002834197,0.0004304618,0.0001696717,0.0001240747,0.00003695851,0.00002054807,0.0001013555,0.0005950986],"category_scores_gemma":[0.0002505879,0.0002634895,0.00009554097,0.0002538633,0.00009673068,0.0002150306,0.0002082645,0.0002960108,0.0001305191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096445,"about_ca_system_score_gemma":0.0001744938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006038266,"about_ca_topic_score_gemma":0.0002477006,"domain_scores_codex":[0.997905,0.00008908608,0.0006141631,0.0004993308,0.0005439563,0.0003484862],"domain_scores_gemma":[0.9990013,0.00007492475,0.0001639575,0.000244199,0.0002143712,0.000301227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003857972,0.0001953399,0.6840234,0.0001129989,0.0001969292,0.0001919288,0.00310236,0.0002455334,0.006525766,0.00001768908,0.0001418679,0.3048604],"study_design_scores_gemma":[0.002074361,0.0007868785,0.8924596,0.0002877026,0.00006938782,0.0008780779,0.0007191026,0.0004672061,0.08609955,0.000006971097,0.01585544,0.0002956651],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959214,0.000250599,0.002012454,0.0002980748,0.0007834735,0.0003628509,0.00002930514,0.00009361211,0.0002481851],"genre_scores_gemma":[0.9841756,0.00004783265,0.01457128,0.0001286865,0.0001076973,0.00004593424,0.00005061669,0.00004103405,0.0008313084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3045647,"threshold_uncertainty_score":0.9999818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009436733506694132,"score_gpt":0.2330165249057404,"score_spread":0.2235797913990462,"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."}}