{"id":"W4210506570","doi":"10.1101/2022.02.03.479059","title":"Transfer Learning for Cognitive Reserve Quantification","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Biogen; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Human Connectome Project; Alzheimer's Disease Neuroimaging Initiative; Generalizability theory; Transfer of learning; Neuroimaging; Artificial intelligence; Cognition; Psychology; Deep learning; Machine learning; Neuroscience; Computer science; Cognitive impairment; Functional connectivity; Developmental psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002831478,0.001162805,0.000658593,0.00108633,0.0002396227,0.0006792371,0.001177287,0.001082067,0.002140217],"category_scores_gemma":[0.00863663,0.0002883262,0.0007953679,0.0007304142,0.0007896003,0.0008884574,0.00123596,0.001650668,0.0005719521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354845,"about_ca_system_score_gemma":0.0009917812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007560192,"about_ca_topic_score_gemma":0.00333231,"domain_scores_codex":[0.999493,0.0001635506,0.00002699932,0.0001527135,0.0001034774,0.00006020976],"domain_scores_gemma":[0.9980587,0.001100412,0.0001857466,0.0002243076,0.0003640809,0.00006677084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002140574,0.0001867131,0.007155014,0.000112649,0.0001997555,0.0001655903,0.00007961213,0.8208238,0.00341904,0.006103795,0.002571312,0.1589686],"study_design_scores_gemma":[0.000003517018,0.00002495778,0.0007394322,0.000007759332,0.000006626201,0.00001266433,0.000005413974,0.9938361,0.0007612748,0.004412317,0.0001841243,0.000005821589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09306977,0.000901046,0.9006135,0.0005147291,0.00007427004,0.0001501802,0.0005661753,0.002076357,0.002033847],"genre_scores_gemma":[0.9258406,0.0002334296,0.06984936,0.0001800008,0.0000503101,0.0002263008,0.0006545845,0.0001161692,0.002849275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007560192,"threshold_uncertainty_score":0.01503235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03786191146893444,"score_gpt":0.3052765585184058,"score_spread":0.2674146470494714,"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."}}