{"id":"W4316037230","doi":"10.21203/rs.3.rs-2468362/v1","title":"Applying an ICD-10-CA to ICD-11 Mapping Tool to Identify Causes of Death Codes in an Alberta Dataset","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Health Services; University of Calgary; University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"ICD-10; Coding (social sciences); Diagnosis code; Disease control; Globe; Population; Cause of death; Medicine; Disease; Demography; Geography; Statistics; Environmental health; Mathematics; Internal medicine; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0135881,0.0003197865,0.0007969211,0.001959741,0.001009756,0.0001069577,0.001209199,0.0007586644,0.001220632],"category_scores_gemma":[0.008234849,0.0003082695,0.0000635637,0.001181099,0.00006284587,0.0003807295,0.002652335,0.003267589,0.002365245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008000034,"about_ca_system_score_gemma":0.002198932,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05457268,"about_ca_topic_score_gemma":0.06516483,"domain_scores_codex":[0.991053,0.002542251,0.00183357,0.00086152,0.00201743,0.001692253],"domain_scores_gemma":[0.9931818,0.002644783,0.0003135808,0.001729782,0.000906698,0.001223416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009317526,0.0005910253,0.2916848,0.07765657,0.00008355153,0.0001079473,0.07701034,0.003406003,0.0007744567,0.00390252,0.5274951,0.0163559],"study_design_scores_gemma":[0.001661351,0.001017944,0.4705599,0.03505599,0.00002752811,0.000001891804,0.04364189,0.01052237,0.0001032429,0.002384773,0.4337746,0.001248574],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9717687,0.00006693834,0.001303137,0.005296779,0.0008039612,0.01170707,0.007964483,0.0001857572,0.0009031763],"genre_scores_gemma":[0.9361546,0.0003148096,0.003350093,0.002596813,0.001363086,0.01421259,0.03819021,0.0001823565,0.003635453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.178875,"threshold_uncertainty_score":0.9999369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6028667104290785,"score_gpt":0.6218629379501303,"score_spread":0.01899622752105179,"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."}}