{"id":"W2982579056","doi":"10.20900/jpbs.20190017","title":"Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis","year":2019,"lang":"en","type":"article","venue":"Journal of Psychiatry and Brain Science","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"National Institute of Neurological Disorders and Stroke; National Institute on Aging; Canadian Institutes of Health Research; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Machine learning; Frontotemporal dementia; Artificial intelligence; Neuroimaging; Computer science; Medical diagnosis; Convolutional neural network; Neuroinformatics; Medical physics; Dementia; Disease; Medicine; Neuroscience; Psychology; Data science; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01513155,0.001463503,0.001375579,0.001581638,0.001884494,0.002639288,0.003463147,0.003191298,0.09650622],"category_scores_gemma":[0.02229274,0.0005049201,0.001280841,0.001228328,0.0009652205,0.002134034,0.005315842,0.003378706,0.05486141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001740473,"about_ca_system_score_gemma":0.01607713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02143089,"about_ca_topic_score_gemma":0.01748909,"domain_scores_codex":[0.9932811,0.001407021,0.0002645392,0.000824096,0.003192606,0.001030548],"domain_scores_gemma":[0.9558609,0.006704459,0.000792832,0.002498436,0.02128212,0.01286129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008126109,0.0006607664,0.004351851,0.00009883718,0.00006081649,0.0001795673,0.00005713496,0.0006005934,0.001000628,0.002106278,0.9247004,0.06537058],"study_design_scores_gemma":[0.002566432,0.002588448,0.01962609,0.0003679093,0.0001492645,0.0007000106,0.0003645989,0.009133195,0.00499037,0.008665346,0.9507066,0.0001417421],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.03434014,0.005791819,0.06285905,0.1947051,0.0414528,0.02010807,0.3818071,0.007773674,0.2511623],"genre_scores_gemma":[0.09633484,0.005898657,0.08866657,0.01947512,0.009982357,0.01232818,0.4623377,0.004269906,0.3007067],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09650622,"threshold_uncertainty_score":0.3228454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016031992401164,"score_gpt":0.3005130628936731,"score_spread":0.2903527429696615,"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."}}