{"id":"W3009759708","doi":"10.3340/jkns.2019.0203","title":"Can Computed Tomographic Angiography Be Used to Predict Who Will Not Benefit from Endovascular Treatment in Patients with Acute Ischemic Stroke? The CTA-ABC Score","year":2020,"lang":"en","type":"article","venue":"Journal of Korean Neurosurgical Society","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jeonbuk National University","keywords":"Medicine; Receiver operating characteristic; Confidence interval; Cohort; Logistic regression; Stroke (engine); Computed tomographic angiography; Angiography; Radiology; Area under the curve; Computed tomographic; Middle cerebral artery; Internal medicine; Computed tomography; Ischemia","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001823122,0.0004362596,0.0009554779,0.0001271959,0.0001028297,0.00005968331,0.0004235644,0.0001321059,0.00004112966],"category_scores_gemma":[0.00003460565,0.000254449,0.001653654,0.001035159,0.0001730157,0.0001283441,0.0001663241,0.0006488438,0.000001161076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001625283,"about_ca_system_score_gemma":0.000076141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008014544,"about_ca_topic_score_gemma":0.00001188598,"domain_scores_codex":[0.9968432,0.00008090438,0.0007721114,0.0005010008,0.001324702,0.0004781211],"domain_scores_gemma":[0.9981166,0.0001782914,0.0004157598,0.0004483495,0.0002043432,0.000636649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002631122,0.0008413732,0.9562147,0.00002986374,0.005681895,0.0006960536,0.002412591,0.0003842128,0.003186394,0.000008001379,0.02676761,0.001146192],"study_design_scores_gemma":[0.01959836,0.005082647,0.9419459,0.0002175084,0.002731088,0.00005431008,0.000269153,0.0007158001,0.002706187,0.000003463268,0.02629361,0.0003820416],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785964,0.00013388,0.0002157701,0.01953916,0.0001020103,0.0009837971,0.0002858716,0.00004186703,0.0001012623],"genre_scores_gemma":[0.9910418,0.0001388673,0.001144592,0.007176321,0.0003107744,0.00001726784,0.00007939368,0.00005564844,0.00003533374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01696724,"threshold_uncertainty_score":0.9999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01556560387213193,"score_gpt":0.217590897496487,"score_spread":0.202025293624355,"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."}}