{"id":"W2105999235","doi":"10.1007/bf03404347","title":"The impact of non-fee-for-service reimbursement on chronic disease surveillance using administrative data.","year":2010,"lang":"en","type":"article","venue":"PubMed","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Reimbursement; Medicine; Medical prescription; Diabetes mellitus; Cohort; Family medicine; Population; Health care; Disease; Fee-for-service; Emergency medicine; Internal medicine; Environmental health; Nursing","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":[0.002875428,0.0001062695,0.0001702046,0.00003443407,0.0007927021,0.00001084103,0.0003636628,0.00009220854,0.00003970975],"category_scores_gemma":[0.001463549,0.00006398495,0.00004213254,0.0001370116,0.00005489993,0.0001129434,0.00008790517,0.0005162656,0.00001825511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139257,"about_ca_system_score_gemma":0.001363659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003499517,"about_ca_topic_score_gemma":0.000654684,"domain_scores_codex":[0.9983273,0.0001475301,0.0005380741,0.0001665866,0.0002580864,0.0005624635],"domain_scores_gemma":[0.9972051,0.001082569,0.0004162295,0.0007112026,0.000208749,0.0003760977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005965678,0.0005586421,0.1758005,0.008010683,0.0004125149,0.000004394427,0.005170871,0.000760267,0.0004005027,0.009410393,0.08153678,0.7119688],"study_design_scores_gemma":[0.0009187329,0.00009049725,0.9601853,0.0001017624,0.00001395472,1.944965e-7,0.0001577112,0.02814016,0.00001145701,0.0002126744,0.01006401,0.0001035605],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814463,0.0001032855,0.0005468434,0.004964171,0.001735855,0.004422384,0.0006408841,0.00004545155,0.006094823],"genre_scores_gemma":[0.9976574,0.00004490669,0.00007515959,0.0003656477,0.0005878083,0.0009650058,0.0001556856,0.00001077295,0.0001376625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7843848,"threshold_uncertainty_score":0.6096904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4495295140856163,"score_gpt":0.531693271290086,"score_spread":0.08216375720446972,"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."}}