{"id":"W2082416963","doi":"10.1002/jmri.21507","title":"Identifying lesion growth with MR imaging in acute ischemic stroke","year":2008,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; Hotchkiss Brain Institute; Ontario Brain Institute; University of Calgary","funders":"Canadian Institutes of Health Research; Fondation pour la Recherche Médicale; Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society of Canada; Heart and Stroke Foundation of Canada","keywords":"Lesion; Medicine; Diffusion MRI; Stroke (engine); Radiology; Perfusion; Voxel; Acute stroke; Magnetic resonance imaging; Abnormality; Receiver operating characteristic; Effective diffusion coefficient; Nuclear medicine; Cerebral blood flow; Pathology; Cardiology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001021644,0.0006198573,0.0003485429,0.001053427,0.0001861303,0.0004474948,0.0002539537,0.0004285505,0.0004909699],"category_scores_gemma":[0.01114284,0.0001460595,0.0001718265,0.000433284,0.0004734103,0.0006096878,0.0004030936,0.0003591354,0.0002102769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002576532,"about_ca_system_score_gemma":0.0003310169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002004651,"about_ca_topic_score_gemma":0.002233732,"domain_scores_codex":[0.9996622,0.0001460897,0.00003827568,0.00005485804,0.00005982693,0.00003887499],"domain_scores_gemma":[0.996959,0.001439218,0.0009776894,0.0001206219,0.0003178242,0.000185742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004911776,0.00006400194,0.9807762,0.00002462497,0.00002834014,0.0001771781,0.0001122431,0.001955251,0.001058828,0.00002705705,0.0001157443,0.0151693],"study_design_scores_gemma":[0.00002607553,0.0006299554,0.969415,0.00002777001,0.00005747834,0.001670872,0.0002188384,0.02434736,0.002888815,0.0004299765,0.0002706728,0.00001706813],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984847,0.0001323942,0.001094736,0.00003316224,0.000001778805,0.00001231697,0.00004563546,0.00002270595,0.0001726041],"genre_scores_gemma":[0.9987207,0.00004746726,0.001018317,0.000006441896,0.000003883221,0.000009227449,0.0001284785,0.000003650207,0.00006174942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002004651,"threshold_uncertainty_score":0.005402982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01414365928059702,"score_gpt":0.2604716289407685,"score_spread":0.2463279696601715,"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."}}