{"id":"W2888949922","doi":"10.1016/j.nicl.2018.08.028","title":"Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability","year":2018,"lang":"en","type":"review","venue":"NeuroImage Clinical","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Fonds de Recherche du Québec - Santé","keywords":"Frontotemporal dementia; Biomarker; Dementia; Medicine; Diagnostic accuracy; Psychology; Machine learning; Atrophy; Medical physics; Pathology; Radiology; Disease; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01391228,0.0009723637,0.01127731,0.0007147007,0.0000817566,0.00006281942,0.001101021,0.0007110229,0.001173997],"category_scores_gemma":[0.09480505,0.0006923343,0.004424798,0.002317282,0.0008516026,0.00009903641,0.001098868,0.001571005,0.003276129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001050268,"about_ca_system_score_gemma":0.001180389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001849305,"about_ca_topic_score_gemma":0.000003697906,"domain_scores_codex":[0.9809155,0.005047623,0.009085146,0.002311148,0.001726113,0.0009144563],"domain_scores_gemma":[0.9747522,0.01694261,0.002360082,0.003318098,0.001099964,0.001526994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001261835,0.002680743,0.01151068,0.7943765,0.001474721,0.0006815348,0.000002116974,6.822272e-10,2.194337e-7,0.000001472156,0.01568286,0.173463],"study_design_scores_gemma":[0.001932466,0.006486042,0.01387619,0.5771214,0.03553699,0.0004461606,0.000003894769,0.000008735295,6.023275e-7,0.00001371394,0.3639127,0.0006610577],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005226288,0.9777148,0.0003310831,0.0002890105,0.0006290991,0.01932266,0.0001207976,0.00009403477,0.0009758445],"genre_scores_gemma":[0.0003180859,0.9932776,0.0008218662,0.00226004,0.0005183113,0.001815028,0.000166779,0.0001533185,0.0006689436],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3482299,"threshold_uncertainty_score":0.9997391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1999430753312786,"score_gpt":0.5118837628248506,"score_spread":0.311940687493572,"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."}}