{"id":"W4414128197","doi":"10.26633/rpsp.2025.95","title":"Preparing for ICD-11 transition: lessons from case studies in Argentina and Mexico","year":2025,"lang":"en","type":"article","venue":"Revista Panamericana de Salud Pública","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information; Cargill (Canada)","funders":"Pan American Health Organization","keywords":"Digital health; Health informatics; Task (project management); Health care; Health policy; Global health; Developing country; Informatics","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":[],"consensus_categories":[],"category_scores_codex":[0.001241871,0.0001528672,0.0005009625,0.0002058773,0.0007115735,0.00001762562,0.00009543754,0.0001288607,0.00004861472],"category_scores_gemma":[0.001318018,0.0001410188,0.00005643325,0.0004531826,0.0001216391,0.00009488666,0.00005689837,0.0003743363,0.000007984494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000321386,"about_ca_system_score_gemma":0.00044963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00276021,"about_ca_topic_score_gemma":0.001674002,"domain_scores_codex":[0.9979171,0.000362164,0.0007843075,0.0002887376,0.0001285378,0.0005191261],"domain_scores_gemma":[0.9974877,0.001709507,0.0002250721,0.000259244,0.0001213039,0.0001971334],"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":[0.0008058827,0.0003463932,0.2411132,0.02747705,0.0006312694,0.0003635267,0.09320539,0.0001238817,0.0001634511,0.1247218,0.3444271,0.166621],"study_design_scores_gemma":[0.003335293,0.0001611438,0.02409986,0.0045095,0.0002802055,0.00004009143,0.03723834,0.01693502,0.00001600926,0.003319197,0.9095256,0.0005397829],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8782297,0.007280311,0.041256,0.06474753,0.0005915629,0.003098932,0.0003141924,0.0002731286,0.004208595],"genre_scores_gemma":[0.9863616,0.001384327,0.002778239,0.008391138,0.0001524894,0.0005585354,0.00006074894,0.00001476527,0.0002982225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5650984,"threshold_uncertainty_score":0.5750579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2459436461507118,"score_gpt":0.5199659631702498,"score_spread":0.2740223170195379,"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."}}