{"id":"W7116792075","doi":"10.33137/jns.v4i1.43735","title":"Quantifying Language Tract Damage in Stroke Patients: Utilizing Diffusion Tractography and Streamline Counts to Predict Aphasia Scores","year":2025,"lang":"","type":"article","venue":"UTSC s Journal of Natural Sciences","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; The Scarborough Hospital; University of Toronto","funders":"","keywords":"Aphasia; White matter; Grey matter; Diffusion MRI; Stroke (engine); Tractography; Fractional anisotropy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0009994955,0.0002443616,0.000394242,0.00112306,0.0003074718,0.000241871,0.0007177774,0.0001171583,0.00005190241],"category_scores_gemma":[0.001221773,0.0001720876,0.0001379512,0.001378472,0.0005812723,0.0005853981,0.0001548581,0.0007199956,0.000002469408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003040938,"about_ca_system_score_gemma":0.0001340662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004382362,"about_ca_topic_score_gemma":0.00009932328,"domain_scores_codex":[0.9975476,0.0002461282,0.0006956076,0.0004632527,0.0005989674,0.0004483938],"domain_scores_gemma":[0.998592,0.0006152043,0.0004149431,0.0001352842,0.0001073648,0.0001352483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000279134,0.0007145522,0.5900722,0.00009216159,0.00002002121,0.001660579,0.003487667,0.00003046922,0.3129925,0.0001080116,0.0003007698,0.09024196],"study_design_scores_gemma":[0.0027234,0.00215112,0.9498883,0.002081645,0.0001114614,0.0005205605,0.005390855,0.001062842,0.03413824,0.00008661244,0.001267001,0.0005779456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911521,0.005454564,0.00000632995,0.0004841018,0.002024425,0.0002209617,0.00003960928,0.00001109894,0.0006067987],"genre_scores_gemma":[0.998037,0.000681911,0.000206671,0.0008239727,0.000105071,5.082321e-7,0.000001194764,0.000006390973,0.0001372165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3598162,"threshold_uncertainty_score":0.7017528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393607496109973,"score_gpt":0.3334049321589154,"score_spread":0.3094688571978156,"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."}}