{"id":"W2194834710","doi":"10.3171/2015.6.jns142203","title":"Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas","year":2015,"lang":"en","type":"article","venue":"Journal of neurosurgery","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"National Institute of Neurological Disorders and Stroke; National Cancer Institute; National Defense Science and Engineering Graduate; National Institutes of Health; U.S. Department of Defense","keywords":"Diffusion MRI; Medicine; Tractography; White matter; Segmentation; Glioma; Fiber tract; Surgical planning; Brain mapping; Neuroscience; Radiology; Magnetic resonance imaging; Artificial intelligence; Computer science; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001975196,0.0002357505,0.0001619695,0.0006274969,0.0001856781,0.0002533619,0.000126766,0.0002662996,0.0005379828],"category_scores_gemma":[0.001432419,0.0001077479,0.0001361804,0.0002273121,0.0002403597,0.0003394522,0.0001825698,0.0002215234,0.0001835193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230565,"about_ca_system_score_gemma":0.0002058507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006229535,"about_ca_topic_score_gemma":0.0109745,"domain_scores_codex":[0.9999367,0.00001018303,0.00000988888,0.00001656641,0.00001322488,0.0000134618],"domain_scores_gemma":[0.9995837,0.0001274319,0.0001243347,0.00003261523,0.00005517129,0.00007684022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002507294,0.00005520113,0.9881577,0.000009720052,0.00001236587,0.0003387866,0.0001823102,0.0004146372,0.001405805,0.00001097931,0.00008978667,0.009071904],"study_design_scores_gemma":[0.00001174966,0.0003455343,0.9962883,0.000006049628,0.00001865654,0.0007482475,0.0003587701,0.001304214,0.0007626116,0.00003319803,0.0001144864,0.000008128485],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997414,0.00003434667,0.00008582203,0.00001068546,8.346373e-7,0.000002617061,0.00002510325,0.000004457459,0.00009470397],"genre_scores_gemma":[0.9996295,0.00004545913,0.0001577783,0.00000528063,0.000001389966,0.000003422527,0.00007607165,0.000001225146,0.00007982214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006229535,"threshold_uncertainty_score":0.0123865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08442622157027459,"score_gpt":0.3243280770015919,"score_spread":0.2399018554313173,"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."}}