{"id":"W2289648021","doi":"10.3390/ijerph13020168","title":"Association between Floods and Acute Cardiovascular Diseases: A Population-Based Cohort Study Using a Geographic Information System Approach","year":2016,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Institut National de la Recherche Scientifique; Centre hospitalier universitaire de Québec; Institut National de Santé Publique du Québec; Bishop's University; Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"","keywords":"Medicine; Logistic regression; Population; Odds ratio; Cohort; Odds; Environmental health; Demography; Cohort study; Disease; Flood myth; Geography; Pathology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001385935,0.0003360387,0.0003202958,0.001450617,0.001141939,0.0008157911,0.0005769377,0.0004422347,0.001445065],"category_scores_gemma":[0.001689238,0.0003167291,0.0007605706,0.002653567,0.000423218,0.0004449182,0.0006072532,0.0006510367,0.0001463327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002463321,"about_ca_system_score_gemma":0.003476484,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5721462,"about_ca_topic_score_gemma":0.5951483,"domain_scores_codex":[0.9992766,0.0001962898,0.00004762989,0.0001485508,0.0001436397,0.0001872293],"domain_scores_gemma":[0.9989833,0.0001671928,0.0002188018,0.0001285566,0.0002786882,0.0002235335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009561108,0.00005508167,0.9985729,0.000009932744,0.000107702,0.00005439335,0.0001435524,0.00004480859,0.0001415893,0.00002153627,0.0001445479,0.0006083684],"study_design_scores_gemma":[0.00001653033,0.0001374664,0.998813,0.000009839689,0.00006321725,0.00004918652,0.0003521146,0.0003067551,0.00003777893,0.00001528373,0.0001919993,0.000006759256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978801,0.00008707715,0.000224115,0.00007075454,0.000006764339,0.00007020594,0.00142847,0.000003602236,0.0002290039],"genre_scores_gemma":[0.9983865,0.00009630337,0.0003871913,0.00004204427,0.000006646892,0.00006502376,0.0007754395,0.000001768654,0.000239014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5721462,"threshold_uncertainty_score":0.860747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02607868112362444,"score_gpt":0.3147622090684817,"score_spread":0.2886835279448573,"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."}}