{"id":"W3200222177","doi":"10.21203/rs.3.rs-882691/v1","title":"Cognitive Composites for Genetic Frontotemporal Dementia: GENFI-Cog","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Western University; University of Toronto; Université Laval","funders":"Medical Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Stichting Dioraphte; Wolfson Foundation; UK Dementia Research Institute; EU Joint Programme – Neurodegenerative Disease Research; National Institute for Health and Care Research; ZonMw; Alzheimer's Society; Brain Research UK","keywords":"Cog; Frontotemporal dementia; Cognition; Composite material; Dementia; Materials science; Psychology; Medicine; Psychiatry; Computer science; Artificial intelligence; Disease; Internal medicine","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":[0.005703514,0.001024322,0.001129787,0.0008567109,0.0001871682,0.0005916483,0.0006959447,0.0004855054,0.002748226],"category_scores_gemma":[0.006166522,0.0001510238,0.001807939,0.000502734,0.0003004782,0.0003156442,0.0004683341,0.0008860143,0.0004997801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007800221,"about_ca_system_score_gemma":0.001120969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488876,"about_ca_topic_score_gemma":0.002420898,"domain_scores_codex":[0.9992525,0.000386795,0.00006519061,0.00009252226,0.0001503691,0.00005261734],"domain_scores_gemma":[0.9980186,0.001107077,0.0003840722,0.00009669842,0.0002253531,0.0001682025],"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":[0.2378505,0.004535832,0.3106988,0.004873703,0.009900956,0.0004241275,0.000390029,0.02352966,0.006419899,0.00185849,0.04105192,0.358466],"study_design_scores_gemma":[0.03074583,0.03849631,0.8668411,0.0005708555,0.007433066,0.001049038,0.0001148135,0.02555552,0.005880546,0.005433478,0.01767879,0.0002006213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9591113,0.008573267,0.006993357,0.001119698,0.0002694862,0.003270941,0.0111347,0.0009626,0.008564667],"genre_scores_gemma":[0.9496903,0.002038124,0.02533025,0.0006938861,0.0001463448,0.006665608,0.01230062,0.00008337689,0.003051438],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005703514,"threshold_uncertainty_score":0.03016341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08058337450509977,"score_gpt":0.4386900870733529,"score_spread":0.3581067125682532,"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."}}