{"id":"W2100064617","doi":"10.1002/jmri.20116","title":"A comparison of images generated from diffusion‐weighted and diffusion‐tensor imaging data in hyper‐acute stroke","year":2004,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Alberta Heritage Foundation for Medical Research; Fondation pour la Recherche Médicale; Multiple Sclerosis Society; Multiple Sclerosis Society of Canada; Heart and Stroke Foundation of Canada","keywords":"Diffusion MRI; Fractional anisotropy; Effective diffusion coefficient; Medicine; Anisotropy; Stroke (engine); Magnetic resonance imaging; Isotropy; Acute stroke; Nuclear medicine; Diffusion; Nuclear magnetic resonance; Radiology; Physics; Internal medicine; Optics","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":[0.001130598,0.00025653,0.0001892583,0.0008926896,0.0001130868,0.0004904633,0.000180371,0.0002838349,0.0005745315],"category_scores_gemma":[0.007676336,0.0001218795,0.0002035892,0.000340287,0.0002565396,0.0003953927,0.0002582528,0.0001382677,0.0001381501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171101,"about_ca_system_score_gemma":0.0001628674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004197179,"about_ca_topic_score_gemma":0.0005446344,"domain_scores_codex":[0.9995573,0.000162594,0.00004733819,0.00006275503,0.0001263753,0.00004353594],"domain_scores_gemma":[0.9979048,0.0009849031,0.0004229413,0.0001592301,0.0004171054,0.0001111421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01324902,0.0007439858,0.4957511,0.0009157275,0.001026423,0.001688987,0.001489025,0.01282885,0.2112839,0.0006265173,0.00100984,0.2593867],"study_design_scores_gemma":[0.0001539367,0.001661543,0.9491278,0.00003884013,0.0002376468,0.003053803,0.0003270595,0.01448893,0.02967593,0.0003779658,0.0008192891,0.0000371311],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973591,0.0001727512,0.002121387,0.00001693263,0.000005691731,0.00002206838,0.00007981541,0.00002146884,0.0002006397],"genre_scores_gemma":[0.9969964,0.0001148696,0.002409962,0.00001148832,0.00001038772,0.00002201342,0.0003255538,0.00001268479,0.00009670557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001130598,"threshold_uncertainty_score":0.00597924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01815252346276071,"score_gpt":0.2944581476155666,"score_spread":0.2763056241528059,"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."}}