{"id":"W4402926610","doi":"10.1186/s12911-024-02661-6","title":"Coding rules for uncertain and “ruled out” diagnoses in ICD-10 and ICD-11","year":2024,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Agency for Healthcare Research and Quality","keywords":"ICD-10; Coding (social sciences); Medical diagnosis; Documentation; Health informatics; Diagnosis code; Computer science; Medicine; Data science; Public health; Nursing; Sociology; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004416735,0.0001792718,0.0004031482,0.0003319935,0.0005595381,0.0001181761,0.0001120204,0.0003797887,0.0002621338],"category_scores_gemma":[0.005328517,0.0001294364,0.00003508204,0.0001506438,0.0001157836,0.0003701891,0.000224968,0.0005479975,0.00004392889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006491233,"about_ca_system_score_gemma":0.0004501024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002521937,"about_ca_topic_score_gemma":0.000365273,"domain_scores_codex":[0.9971554,0.00009699442,0.001480293,0.0001711127,0.0006101526,0.0004860936],"domain_scores_gemma":[0.9852313,0.01393093,0.0001923018,0.0001391297,0.00008010938,0.0004262537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001925547,0.00002252964,0.01096796,0.0116107,0.00001187679,0.0000112868,0.01544773,0.00001847948,7.066013e-7,0.03219259,0.02097899,0.9085446],"study_design_scores_gemma":[0.001805775,0.00009761676,0.00324443,0.0106966,0.00001915638,0.00001448691,0.006611557,0.9036519,6.057174e-7,0.0140818,0.05955945,0.0002166132],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5611024,0.0021493,0.4318758,0.0006998582,0.0009065185,0.001025212,0.00003914196,0.0001274779,0.002074318],"genre_scores_gemma":[0.8136573,0.006052554,0.1706148,0.008136849,0.0007029578,0.0003966817,0.00008242089,0.00004588878,0.0003105681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.908328,"threshold_uncertainty_score":0.6379117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2494618248725323,"score_gpt":0.5092653510982413,"score_spread":0.2598035262257091,"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."}}