{"id":"W2106241798","doi":"10.1016/s0720-048x(02)00305-4","title":"Diffusion weighted magnetic resonance imaging in stroke","year":2003,"lang":"en","type":"review","venue":"European Journal of Radiology","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":113,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Canada Research Chairs","keywords":"Penumbra; Medicine; Magnetic resonance imaging; Diffusion MRI; Stroke (engine); Artifact (error); Effective diffusion coefficient; Radiology; Diffusion; Hyperintensity; Diffusion imaging; Ischemia; Artificial intelligence; Cardiology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005698327,0.0002604311,0.001262468,0.0004351309,0.0000331576,0.000007768995,0.0002825914,0.00004800864,0.00004895324],"category_scores_gemma":[0.00009325772,0.000194803,0.000314121,0.0002496158,0.0001143504,0.00003560817,0.00005446244,0.001008807,0.00002401407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031225,"about_ca_system_score_gemma":0.0001139497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.858956e-7,"about_ca_topic_score_gemma":6.8807e-8,"domain_scores_codex":[0.9975936,0.0007657643,0.00101215,0.0002600268,0.00011797,0.0002504454],"domain_scores_gemma":[0.9986772,0.000118357,0.0006554036,0.0003614493,0.00007138585,0.0001162098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000171682,0.00005534411,0.0001679714,0.0003054192,0.000004837165,0.002089246,0.000009599019,9.87555e-8,0.0000245416,0.000136964,0.002751229,0.9944376],"study_design_scores_gemma":[0.0006357101,0.0002552103,0.0003725459,0.004375138,0.0001819925,0.0229471,0.000002932329,0.000005603463,8.563869e-7,0.000059292,0.9710287,0.0001349391],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004607868,0.9937672,0.001933319,0.0001764008,0.000156761,0.0002956538,0.000007793112,0.00002611409,0.003590653],"genre_scores_gemma":[0.00003213238,0.9850667,0.01379779,0.0002335027,0.0002334165,0.000004272803,0.000007651282,0.00007806031,0.0005464691],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9943026,"threshold_uncertainty_score":0.7943836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05855231441885334,"score_gpt":0.3503795105159146,"score_spread":0.2918271960970613,"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."}}