{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0101817,0.0004886236,0.0004751069,0.001238823,0.007817723,0.003374843,0.00160638,0.00205623,0.002361822],"category_scores_gemma":[0.01775507,0.0003317498,0.0004718593,0.00175767,0.003148753,0.002842934,0.003619047,0.002886277,0.0001092245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01415015,"about_ca_system_score_gemma":0.007882603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1002683,"about_ca_topic_score_gemma":0.1924569,"domain_scores_codex":[0.9900202,0.007708262,0.0002929642,0.0003281723,0.0004952983,0.00115518],"domain_scores_gemma":[0.9911723,0.005300264,0.001016277,0.0003348778,0.001261031,0.0009152216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002399591,0.001000719,0.126062,0.001134696,0.00007228644,0.03562534,0.7085932,0.001198623,0.0009181588,0.02684004,0.02196259,0.07635242],"study_design_scores_gemma":[0.00003899928,0.0001522196,0.04568968,0.001843282,0.00005049689,0.003038317,0.8817892,0.0004626938,0.0004071332,0.00249125,0.06398638,0.0000502295],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9169216,0.00628004,0.004273945,0.04009559,0.0004097096,0.000410384,0.0003235181,0.00003299264,0.03125237],"genre_scores_gemma":[0.9815456,0.005604336,0.005900541,0.002453209,0.0001264197,0.0005002562,0.0002302416,0.00003227909,0.003607058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1002683,"threshold_uncertainty_score":0.1993694,"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."}}