{"id":"W2743733667","doi":"10.1136/neurintsurg-2017-013224","title":"Pretreatment predictors of malignant evolution in patients with ischemic stroke undergoing mechanical thrombectomy","year":2017,"lang":"en","type":"article","venue":"Journal of NeuroInterventional Surgery","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministero della Salute","keywords":"Medicine; Stroke (engine); Occlusion; Predictive value; Internal medicine; Cardiology; Radiological weapon; Blood pressure; Cerebral infarction; Multivariate analysis; Ischemic stroke; Infarction; Radiology; Ischemia; Myocardial infarction","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":[0.0004256204,0.0002711815,0.0003032404,0.0007023836,0.0002984235,0.0004946928,0.00023569,0.0003757467,0.001508146],"category_scores_gemma":[0.00333868,0.0001538366,0.0002592898,0.0006475797,0.000277695,0.0003274881,0.0002856628,0.0005155821,0.0002141393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001486306,"about_ca_system_score_gemma":0.0003228872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007546095,"about_ca_topic_score_gemma":0.00105731,"domain_scores_codex":[0.9997823,0.00005948058,0.00003393073,0.00002996387,0.00004776747,0.00004655701],"domain_scores_gemma":[0.9983777,0.0004772721,0.0006793986,0.00007093235,0.0001055382,0.0002891638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007315943,0.00001356466,0.9993666,0.000001859164,0.000007611291,0.00005754289,0.000009579621,0.00002883783,0.0000509123,0.00000534434,0.00002204472,0.0003631047],"study_design_scores_gemma":[0.000004969994,0.00009662755,0.9989663,0.000003502164,0.00001548631,0.0003706479,0.00004347647,0.0003559733,0.00005539643,0.00002653879,0.00005896116,0.000002106621],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993748,0.0001378728,0.00008246228,0.0000429817,0.00000439943,0.000005431425,0.00009520079,0.000002678276,0.0002542697],"genre_scores_gemma":[0.9996853,0.00004886133,0.00005511584,0.000008922451,0.00001333624,0.000003915098,0.0001544174,7.261248e-7,0.00002938729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001508146,"threshold_uncertainty_score":0.005045235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01959773635933038,"score_gpt":0.2580646696522623,"score_spread":0.2384669332929319,"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."}}