{"id":"W4388887794","doi":"10.1093/braincomms/fcad313","title":"Longitudinal evolution of diffusion metrics after left hemisphere ischaemic stroke","year":2023,"lang":"en","type":"article","venue":"Brain Communications","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Toronto Rehabilitation Institute; Heart and Stroke Foundation; University of Toronto; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; University Health Network; Université de Sherbrooke; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"White matter; Fractional anisotropy; Diffusion MRI; Stroke (engine); Lesion; Medicine; Magnetic resonance imaging; Pathology; Radiology; Physics","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":[0.0002941655,0.0001674609,0.0002825282,0.0005956097,0.0001624363,0.0003239389,0.00006703348,0.0001911116,0.0005412904],"category_scores_gemma":[0.001174727,0.00009031448,0.0001552191,0.0003932437,0.0001567545,0.0002491639,0.0002028599,0.0002088013,0.0001764219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001992156,"about_ca_system_score_gemma":0.0001484694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001819,"about_ca_topic_score_gemma":0.002640908,"domain_scores_codex":[0.9998968,0.00001533999,0.00001328876,0.0000295861,0.00002322479,0.00002184861],"domain_scores_gemma":[0.9994938,0.00008618688,0.0002463358,0.00003985288,0.0000825508,0.00005120819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002201711,0.0001566021,0.7518705,0.0001479178,0.0004404209,0.001896396,0.001424944,0.003432374,0.1593998,0.0001385305,0.0005700462,0.07832076],"study_design_scores_gemma":[0.00000389668,0.0002530296,0.9933423,0.000005404259,0.00003871593,0.0008443581,0.0001033908,0.0009045005,0.004183748,0.00005770561,0.0002515629,0.00001140151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988946,0.0002108787,0.000490899,0.00001331451,0.000001291996,0.000004966861,0.0001933776,0.00001621767,0.000174477],"genre_scores_gemma":[0.9988368,0.0001127284,0.000389762,0.000004251876,0.000003029012,0.000007773025,0.0004608516,0.000006061483,0.000178683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001819,"threshold_uncertainty_score":0.003616869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09061495565941778,"score_gpt":0.3774562144362619,"score_spread":0.2868412587768441,"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."}}