{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001792553,0.0004655291,0.00031839,0.00463415,0.0008617357,0.001248092,0.0013413,0.0004941362,0.007490199],"category_scores_gemma":[0.00755594,0.0003719118,0.0007396945,0.006400861,0.0002984011,0.0002356516,0.0009107027,0.0007768273,0.002626809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009076546,"about_ca_system_score_gemma":0.01876344,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9595017,"about_ca_topic_score_gemma":0.9746191,"domain_scores_codex":[0.9989089,0.0001391439,0.00007021554,0.0001957253,0.0004698522,0.0002161638],"domain_scores_gemma":[0.9952651,0.0008586207,0.0002291133,0.000567682,0.002802554,0.0002770181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006581023,0.0001861791,0.2444444,0.0005366672,0.0004843864,0.00036818,0.0009639778,0.01315739,0.001830885,0.00588497,0.6633751,0.06810985],"study_design_scores_gemma":[0.0002403569,0.00003903914,0.781734,0.0002791258,0.0002146702,0.0001900154,0.001568708,0.01831034,0.00178023,0.002610126,0.1929214,0.0001119215],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1176675,0.0003456403,0.003578802,0.0006013934,0.00008798798,0.0002481802,0.8653437,0.0009733208,0.01115346],"genre_scores_gemma":[0.1197873,0.0002279662,0.01321781,0.0001442425,0.00001959439,0.0001763501,0.857865,0.0002023766,0.008359362],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04049826,"threshold_uncertainty_score":0.08147347,"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."}}