{"id":"W2181306017","doi":"10.1371/journal.pone.0143342","title":"Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT)","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Public Health Ontario; University of Toronto; Statistics Canada; Institute for Clinical Evaluative Sciences; Ottawa Hospital; Bruyère; University of Ottawa","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Public Health Agency; Heart and Stroke Foundation of Canada; Institute for Clinical Evaluative Sciences; Public Health Agency of Canada","keywords":"Medicine; Stroke (engine); Population; Risk assessment; Relative risk; Physical therapy; Emergency medicine; Demography; Environmental health; Confidence interval; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007071271,0.0008902773,0.001076766,0.004890174,0.0002860847,0.001177318,0.001088545,0.0007709306,0.00112149],"category_scores_gemma":[0.02541456,0.0003728251,0.001574076,0.001664996,0.0002596319,0.0008238692,0.001171223,0.0009645834,0.0003417706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004677621,"about_ca_system_score_gemma":0.001258509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002478701,"about_ca_topic_score_gemma":0.003309724,"domain_scores_codex":[0.9963805,0.001728781,0.000524789,0.0002852469,0.0009107049,0.0001700982],"domain_scores_gemma":[0.9902452,0.005942516,0.001145593,0.0002660778,0.002089584,0.0003110234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000674244,0.0007432509,0.7465796,0.0003446264,0.001110164,0.0001651526,0.0003156771,0.008265262,0.0005779656,0.0009596868,0.005767711,0.2344966],"study_design_scores_gemma":[0.0006158774,0.002046591,0.7516937,0.0005627908,0.001042157,0.001269495,0.000431169,0.227017,0.001961718,0.006874857,0.006327709,0.0001569607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8678259,0.001130503,0.1049629,0.001761446,0.0002558231,0.005613922,0.009943894,0.001591556,0.006914061],"genre_scores_gemma":[0.8139873,0.0005114287,0.1725829,0.0002780659,0.0001045019,0.004134028,0.007540441,0.00006016131,0.0008011203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007071271,"threshold_uncertainty_score":0.03739691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04816462394387702,"score_gpt":0.2646338097005818,"score_spread":0.2164691857567047,"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."}}