{"id":"W3089809590","doi":"10.24095/hpcdp.30.1.05f","title":"Utilisation des données administratives pour comprendre la distribution géographique de la détermination des cas","year":2009,"lang":"fr","type":"article","venue":"Maladies chroniques et blessures au Canada","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health; University of Alberta; McMaster University","funders":"","keywords":"Humanities; Political science; Geography; Art","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009939085,0.0005408765,0.0005980866,0.0001282515,0.0004679848,0.0002022837,0.0002796109,0.0003155004,0.0003034915],"category_scores_gemma":[0.0006260811,0.000585232,0.0001603245,0.0003896345,0.001982794,0.0005095482,0.00006413046,0.0004724739,0.000003610698],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003264918,"about_ca_system_score_gemma":0.006210039,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3545642,"about_ca_topic_score_gemma":0.9280903,"domain_scores_codex":[0.9957657,0.001697741,0.0005540213,0.0005583746,0.0004937506,0.0009303649],"domain_scores_gemma":[0.9974813,0.0009434843,0.0002929721,0.0004461605,0.0003515734,0.0004844351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007332308,0.001221894,0.6511187,0.006814168,0.0008335832,0.003148956,0.006356494,0.001103953,0.001997953,0.07682343,0.09622461,0.153623],"study_design_scores_gemma":[0.0008737313,0.0003573824,0.8904288,0.002797583,0.0002719919,0.0003647537,0.001375879,0.0009360524,0.005331211,0.003524905,0.09321436,0.0005233223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.939678,0.02536902,0.003581935,0.004223009,0.0004904057,0.0006957036,0.003828102,0.0002720201,0.02186176],"genre_scores_gemma":[0.9858035,0.007471567,0.001217047,0.0004239839,0.0005504471,0.0000510213,0.002116556,0.00004639705,0.002319451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5735261,"threshold_uncertainty_score":0.9996599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133276456117199,"score_gpt":0.3074611315259924,"score_spread":0.2861283669648204,"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."}}