{"id":"W3174779648","doi":"10.21203/rs.3.rs-505934/v1","title":"The ICD-11 Field Trial: Creating a Large Dually Coded Database","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; University of Calgary","keywords":"Coding (social sciences); ICD-10; Medicine; Database; Data quality; Medical diagnosis; Diagnosis code; Data mining; Computer science; Statistics; Engineering; Pathology; Operations management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.06417947,0.001038672,0.001967066,0.001584079,0.001660979,0.003617935,0.001776534,0.002090222,0.01478347],"category_scores_gemma":[0.1462586,0.001218373,0.001327959,0.001902056,0.001875338,0.002336396,0.003756625,0.002887737,0.004606311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001651869,"about_ca_system_score_gemma":0.006728124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003956117,"about_ca_topic_score_gemma":0.005512732,"domain_scores_codex":[0.941667,0.04786134,0.00349069,0.002918063,0.003090896,0.0009720465],"domain_scores_gemma":[0.9107449,0.05424089,0.007013877,0.01706648,0.00633255,0.004601267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"not_applicable","study_design_scores_codex":[0.3423422,0.01004513,0.09995919,0.005016138,0.004556148,0.001013864,0.002866055,0.003207898,0.00485606,0.0265849,0.3210498,0.1785026],"study_design_scores_gemma":[0.5992684,0.02406504,0.1314152,0.002667943,0.006029913,0.001546924,0.002595246,0.01562779,0.004215455,0.0415259,0.1702955,0.0007466373],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5689435,0.003546478,0.1133498,0.02178306,0.008766353,0.09898539,0.1636194,0.002109671,0.01889627],"genre_scores_gemma":[0.5581532,0.001248632,0.1633429,0.01142275,0.002733261,0.145156,0.104423,0.001296063,0.01222423],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06417947,"threshold_uncertainty_score":0.3394175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4802990170877911,"score_gpt":0.607431988734799,"score_spread":0.1271329716470079,"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."}}