{"id":"W4235690983","doi":"10.1017/cjn.2019.81","title":"GP.05 Intraoperative acquisition of diffusion tensor imaging in cranial neurosurgery: readout-segmented DTI versus standard single-shot DTI","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Hospital Edmonton","funders":"","keywords":"Diffusion MRI; Medicine; White matter; Nuclear medicine; Artifact (error); Magnetic resonance imaging; Tractography; Radiology; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001682617,0.000250934,0.0001758177,0.0002235519,0.00009292317,0.0004290812,0.0003213149,0.00024419,0.005268159],"category_scores_gemma":[0.003936282,0.0001167843,0.0001262535,0.0001975725,0.00033639,0.000422371,0.0003850534,0.0003618911,0.000920531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921659,"about_ca_system_score_gemma":0.0003947479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006966285,"about_ca_topic_score_gemma":0.0007151479,"domain_scores_codex":[0.9996305,0.0001587591,0.00002767378,0.00007096191,0.00008466027,0.00002754126],"domain_scores_gemma":[0.9988618,0.0004983393,0.0001691359,0.0001623488,0.0001967201,0.0001117086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.02515229,0.0007249278,0.09619994,0.0007829711,0.0002656352,0.000819138,0.0004044721,0.004696984,0.06884915,0.001230044,0.009123586,0.7917508],"study_design_scores_gemma":[0.00276269,0.05210669,0.7096885,0.0004588247,0.0006671972,0.019687,0.0006849841,0.08616059,0.09124354,0.00352413,0.03287849,0.0001372217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645603,0.002169591,0.02351381,0.0003965255,0.0001533214,0.0001926716,0.0004237989,0.0006038795,0.007986105],"genre_scores_gemma":[0.9738234,0.0004889171,0.0235942,0.0001043768,0.00009969852,0.0001205342,0.0004930149,0.000128525,0.001147292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005268159,"threshold_uncertainty_score":0.01762372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07378926848671195,"score_gpt":0.3279315574601632,"score_spread":0.2541422889734512,"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."}}