{"id":"W7067280695","doi":"","title":"L'usage secondaire des données médico-administratives afin d’optimiser l’usage des médicaments chez les patients atteints de maladies respiratoires chroniques : adhésion aux médicaments, identification de cas et intensification du traitement","year":2021,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Medication Adherence and Compliance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Ministère de la Santé; Ministère de la Santé et des Services sociaux; AstraZeneca","keywords":"Asthma; Medication adherence; Pharmacy; Medical record; Adverse effect; Disease; Health care; MEDLINE; Medical prescription; Clinical pharmacy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0293832,0.0006330431,0.001033522,0.004927048,0.001124256,0.005762563,0.001355196,0.001557714,0.002802304],"category_scores_gemma":[0.1101539,0.0004952755,0.001233134,0.004643122,0.0005978748,0.002157723,0.001221971,0.001437781,0.0009149637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00344391,"about_ca_system_score_gemma":0.00554141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05823494,"about_ca_topic_score_gemma":0.07037848,"domain_scores_codex":[0.9724458,0.01166199,0.004509871,0.002987289,0.007762227,0.0006329249],"domain_scores_gemma":[0.8193258,0.1371689,0.01569254,0.006565238,0.01991663,0.001330925],"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":[0.001758261,0.0006750765,0.4385517,0.004172456,0.001092827,0.0007469758,0.009595294,0.00338821,0.01266811,0.0017468,0.01547268,0.5101315],"study_design_scores_gemma":[0.0003950958,0.001588832,0.7627503,0.004106776,0.001222374,0.003281493,0.01027569,0.04892588,0.03127181,0.002376649,0.1332824,0.0005227714],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8544464,0.01224701,0.06129633,0.01714988,0.0005169308,0.001380805,0.03636633,0.003493029,0.0131034],"genre_scores_gemma":[0.8720635,0.0029862,0.1079908,0.001921312,0.0001809107,0.0007589305,0.01094471,0.0002148538,0.002938753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05823494,"threshold_uncertainty_score":0.155395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02793612015139601,"score_gpt":0.2512291637019081,"score_spread":0.2232930435505121,"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."}}