{"id":"W6907928793","doi":"10.25384/sage.24053909","title":"sj-docx-1-ine-10.1177_15910199231196614 - Supplemental material for Influence of geography, stroke timing, and weather conditions on transport and workflow times: Results from a longitudinal 5-year Canadian provincial registry","year":2023,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Older Adults Driving Studies","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Workflow; Stroke (engine); Neuroradiology; Longitudinal data; MEDLINE","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00175221,0.001245911,0.001472091,0.005411225,0.002929069,0.004543283,0.004163033,0.001740756,0.877112],"category_scores_gemma":[0.03229772,0.00142313,0.001416432,0.01048051,0.0006318866,0.0028265,0.002613357,0.001622446,0.3946662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008555786,"about_ca_system_score_gemma":0.02148015,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6926085,"about_ca_topic_score_gemma":0.7411329,"domain_scores_codex":[0.9979906,0.00009486362,0.0002917636,0.0002222624,0.0009155276,0.000485133],"domain_scores_gemma":[0.9696323,0.006945696,0.001347092,0.001506147,0.01831773,0.002250999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005286855,0.00002856511,0.001029347,0.0003387537,0.000007706849,0.00001234145,0.00004969639,0.00004272444,0.00002379827,0.0001376072,0.9943855,0.003891066],"study_design_scores_gemma":[0.002004588,0.00009401026,0.05943763,0.002519158,0.00008834934,0.0001297454,0.00162102,0.0005841632,0.0005636782,0.00167172,0.9311053,0.0001806346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0002382853,0.00002621618,0.00008647276,0.0002812908,0.00008366979,0.0001864983,0.9902379,0.0003996047,0.00845999],"genre_scores_gemma":[0.005642978,0.0002872551,0.001663319,0.0008636791,0.0001421323,0.001622725,0.9290967,0.001331638,0.05934946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.877112,"threshold_uncertainty_score":0.6184036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05995204155806261,"score_gpt":0.3754673604404268,"score_spread":0.3155153188823642,"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."}}