{"id":"W2280903028","doi":"10.1136/neurintsurg-2015-012188","title":"ASPECTS discrepancies between CT and MR imaging: analysis and implications for triage protocols in acute ischemic stroke","year":2016,"lang":"en","type":"article","venue":"Journal of NeuroInterventional Surgery","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Triage; Logistic regression; Magnetic resonance imaging; Stroke (engine); Radiology; Multivariate analysis; Multivariate statistics; Diffusion MRI; Neuroimaging; Retrospective cohort study; Emergency medicine; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02593761,0.0003987291,0.0004219702,0.002941389,0.0004085779,0.001081845,0.001088241,0.0006009592,0.0006574942],"category_scores_gemma":[0.1220478,0.0004020511,0.0004875826,0.002505549,0.0006406051,0.00156725,0.001196033,0.0007865405,0.0001679134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008522617,"about_ca_system_score_gemma":0.001070969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002176401,"about_ca_topic_score_gemma":0.001710337,"domain_scores_codex":[0.9825284,0.01137481,0.002671985,0.0007764463,0.002226618,0.00042168],"domain_scores_gemma":[0.8737236,0.07874575,0.03554439,0.003840695,0.0067281,0.001417485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001477372,0.00001300448,0.9968926,0.00001503229,0.0000246325,0.00004373576,0.00008348203,0.0002238606,0.00003889806,0.00002623532,0.00008749968,0.002403297],"study_design_scores_gemma":[0.00001452829,0.0001673809,0.9866338,0.0000839592,0.00006438941,0.0008336575,0.0007322014,0.01073157,0.000231377,0.000235517,0.0002563241,0.00001537591],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965938,0.0008092117,0.001706552,0.0003251254,0.00001946261,0.00004690859,0.0001588532,0.00001144106,0.0003286281],"genre_scores_gemma":[0.9982382,0.0001466407,0.001347186,0.00002336638,0.00002412347,0.00002115053,0.0001755608,0.000005088244,0.00001862863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02593761,"threshold_uncertainty_score":0.1371728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04317828199967037,"score_gpt":0.3402598725990865,"score_spread":0.2970815905994161,"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."}}