{"id":"W2005487413","doi":"10.1002/hbm.21484","title":"Whole‐brain white matter disruption in semantic and nonfluent variants of primary progressive aphasia","year":2011,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University of Toronto; University Health Network; Health Sciences Centre; Toronto Rehabilitation Institute; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; University of Toronto; Heart and Stroke Foundation of Canada","keywords":"White matter; Primary progressive aphasia; Diffusion MRI; Fractional anisotropy; Atrophy; Pathology; Grey matter; Audiology; Psychology; Neuroscience; Voxel-based morphometry; Medicine; Magnetic resonance imaging; Frontotemporal dementia; Radiology; Dementia","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":[0.0003128946,0.0004977474,0.0003519481,0.001317562,0.0004223088,0.0003935739,0.0002059864,0.0004254289,0.0009713304],"category_scores_gemma":[0.001232172,0.0003343392,0.0002421874,0.0004059778,0.0009203075,0.0003833797,0.0004161068,0.0002525022,0.0002022106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002259532,"about_ca_system_score_gemma":0.0001825004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002132422,"about_ca_topic_score_gemma":0.003593207,"domain_scores_codex":[0.9997621,0.00004334015,0.00003729131,0.00008982771,0.00004177225,0.00002572827],"domain_scores_gemma":[0.9995319,0.0001132623,0.00021079,0.00004201454,0.00003357687,0.00006848141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001117569,0.0002209938,0.9164269,0.0001018125,0.0002529426,0.008783106,0.001974456,0.0003553575,0.05489186,0.0001843474,0.0001317082,0.01555899],"study_design_scores_gemma":[0.00002836564,0.000465877,0.9840562,0.000004847025,0.00002708785,0.01395358,0.0002784796,0.000220393,0.0007511334,0.0001453215,0.0000604239,0.000008205485],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998081,0.00003366578,0.00006170626,0.000004589468,7.448309e-7,0.000003155782,0.00001489427,0.000002810815,0.00007033857],"genre_scores_gemma":[0.9997596,0.00002009865,0.0001294695,0.000006528564,0.000001812203,0.00000332014,0.00003353312,0.000001633081,0.00004400303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002132422,"threshold_uncertainty_score":0.004240036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08366397990442903,"score_gpt":0.3340733727070875,"score_spread":0.2504093928026585,"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."}}