{"id":"W2012122949","doi":"10.1186/1472-6963-11-194","title":"Improved accuracy of co-morbidity coding over time after the introduction of ICD-10 administrative data","year":2011,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Medicine; Chart; Kappa; Coding (social sciences); Health administration; Health informatics; Nursing research; ICD-10; Emergency medicine; Pediatrics; Statistics; Public health; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0149131,0.0001417967,0.0004196685,0.0002127541,0.0009213002,0.00001017128,0.0009553456,0.0002216149,0.008634484],"category_scores_gemma":[0.0009803405,0.00009677462,0.00003847092,0.0005623171,0.0002476279,0.0005068478,0.0005198866,0.00130434,0.0004782003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001817553,"about_ca_system_score_gemma":0.003108093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005043841,"about_ca_topic_score_gemma":0.001245717,"domain_scores_codex":[0.9936428,0.002622606,0.001510931,0.0003640082,0.0009876981,0.0008719641],"domain_scores_gemma":[0.9939929,0.002407867,0.0009443207,0.001412066,0.0008776595,0.0003651798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.02279752,0.001651502,0.2227142,0.1639886,0.0002356834,0.000004758905,0.3667186,0.000003335018,0.001981771,0.005390375,0.1871044,0.0274092],"study_design_scores_gemma":[0.002920583,0.002158754,0.7913706,0.002608857,0.00003193031,0.00000425638,0.03547262,0.03928279,0.001050453,0.0005488525,0.1241741,0.0003762188],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716759,0.0006710588,0.0007832056,0.004821569,0.0008454136,0.006297065,0.0009740641,0.0001150753,0.01381659],"genre_scores_gemma":[0.994889,0.00028749,0.0009707694,0.001081867,0.0009033854,0.0001965783,0.0004129934,0.00002112475,0.0012368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5686563,"threshold_uncertainty_score":0.9922718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5146041854955904,"score_gpt":0.583941607519064,"score_spread":0.0693374220234736,"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."}}