{"id":"W7104278979","doi":"10.71781/4701","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":"","keywords":"Asthma; Medication adherence; Pharmacy; Medical record; Adverse effect; Disease; Health care; MEDLINE; Medical prescription; Clinical pharmacy","routes":{"ca_aff":false,"ca_fund":false,"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":[0.03144965,0.0006925137,0.001166881,0.005343809,0.001163537,0.006192876,0.00142714,0.001730782,0.002914113],"category_scores_gemma":[0.1299791,0.0005387022,0.001448172,0.00512467,0.0006354274,0.002375279,0.001290172,0.001741136,0.0009320474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003484494,"about_ca_system_score_gemma":0.006087788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04762609,"about_ca_topic_score_gemma":0.05231609,"domain_scores_codex":[0.9679385,0.01469351,0.005071071,0.003373582,0.008264779,0.0006586608],"domain_scores_gemma":[0.7840548,0.1680191,0.01699426,0.007373812,0.02199806,0.001560044],"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":[0.002001532,0.0008085199,0.4056492,0.005378898,0.001331605,0.0008205155,0.01095253,0.003576396,0.01163452,0.001854369,0.01566692,0.540325],"study_design_scores_gemma":[0.0004627795,0.001834803,0.7450518,0.005656649,0.001487695,0.004091045,0.01083199,0.04864308,0.03041565,0.003058464,0.1478692,0.0005969554],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.844852,0.01463602,0.07002246,0.01818435,0.0005730338,0.001512835,0.03408195,0.00346436,0.01267292],"genre_scores_gemma":[0.8657712,0.003696443,0.1144915,0.002019452,0.0002012479,0.0008982616,0.01009363,0.0002317379,0.002596538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04762609,"threshold_uncertainty_score":0.1663237,"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."}}