{"id":"W4394840488","doi":"10.5435/jaaos-d-23-00839","title":"Applications of Natural Language Processing for Automated Clinical Data Analysis in Orthopaedics","year":2024,"lang":"en","type":"review","venue":"Journal of the American Academy of Orthopaedic Surgeons","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Identification (biology); Data science; Field (mathematics); Process (computing); Artificial intelligence; Health care; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007243876,0.001179562,0.002285123,0.007465034,0.0004740653,0.002662809,0.001629543,0.002064609,0.003965879],"category_scores_gemma":[0.01598038,0.0005916024,0.002350171,0.005182699,0.001333524,0.003031044,0.001595719,0.003231048,0.002035542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001638971,"about_ca_system_score_gemma":0.004328285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003147295,"about_ca_topic_score_gemma":0.003821929,"domain_scores_codex":[0.9969063,0.001268402,0.0005029106,0.0002920075,0.000948511,0.00008188478],"domain_scores_gemma":[0.9812756,0.01624601,0.0006832096,0.0002870965,0.001396604,0.0001114969],"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":[0.00003189024,0.00006587439,0.0002357505,0.05153295,0.0003284319,0.000142565,0.0001943734,0.0007047358,0.0006361662,0.007085471,0.01025161,0.9287902],"study_design_scores_gemma":[0.00004382674,0.0001279408,0.002226574,0.06783363,0.0006044117,0.001415855,0.00028597,0.000905556,0.001085653,0.01405303,0.9113152,0.0001025348],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00009856663,0.994602,0.002209408,0.0008674468,0.00017977,0.00008377495,0.00005553485,0.00003220106,0.001871321],"genre_scores_gemma":[0.001173775,0.9915313,0.006119867,0.0005449579,0.0001581388,0.00009779842,0.00008496282,0.00001065693,0.0002786642],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.007465034,"threshold_uncertainty_score":0.03830969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2199593642670278,"score_gpt":0.5506948246561537,"score_spread":0.3307354603891259,"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."}}