{"id":"W2535326255","doi":"10.1159/000449288","title":"How Reliable Are Administrative Data for Capturing Stroke Patients and Their Care","year":2016,"lang":"en","type":"article","venue":"Cerebrovascular Diseases Extra","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Stroke Network; University Health Network; Institute for Work & Health; Institute for Clinical Evaluative Sciences","funders":"Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Ontario Stroke Network; Heart and Stroke Foundation of Canada","keywords":"Medicine; Stroke (engine); Atrial fibrillation; Emergency medicine; Audit; Intracerebral hemorrhage; Medical record; Kappa; Population; Internal medicine; Medical emergency; Subarachnoid hemorrhage","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1073236,0.0005954925,0.001184028,0.006748904,0.001164097,0.00789269,0.00219949,0.002212884,0.0008899401],"category_scores_gemma":[0.4114053,0.0007875661,0.001436066,0.007593319,0.002832679,0.007348852,0.002249828,0.001664561,0.0007647524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003621069,"about_ca_system_score_gemma":0.007041012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03793134,"about_ca_topic_score_gemma":0.0380847,"domain_scores_codex":[0.8552287,0.08150908,0.01792006,0.01001822,0.0320655,0.003258419],"domain_scores_gemma":[0.4578171,0.2546684,0.09761508,0.05711568,0.1285294,0.004254364],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001229061,0.00007112674,0.9532455,0.0003463303,0.0009113188,0.00003194492,0.00163555,0.001489113,0.0001159025,0.001172028,0.006247218,0.03461111],"study_design_scores_gemma":[0.00008672658,0.000170172,0.9517136,0.00226588,0.000744271,0.0002342066,0.004433004,0.011193,0.001078948,0.005609166,0.02228291,0.0001882217],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8510286,0.01239593,0.05612193,0.02492134,0.001856371,0.0009033229,0.02417309,0.0005038357,0.02809563],"genre_scores_gemma":[0.9702216,0.001834178,0.01988878,0.001571465,0.0005660075,0.0002910327,0.005209936,0.00008119548,0.0003358089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8926764,"threshold_uncertainty_score":0.5675881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0299097731534595,"score_gpt":0.2557872979193392,"score_spread":0.2258775247658797,"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."}}