{"id":"W4390192667","doi":"10.1002/alz.077327","title":"Can we use machine learning to predict cognitive performance from actigraphy data? Preliminary results from the UK Biobank Study","year":2023,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health; University of British Columbia; Vancouver Coastal Health","funders":"","keywords":"Actigraphy; Biobank; Cognitive decline; Psychology; Cognition; Effects of sleep deprivation on cognitive performance; Physical medicine and rehabilitation; Dementia; Circadian rhythm; Medicine; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001182079,0.000398173,0.0002719504,0.00009046851,0.0008184565,0.0001346743,0.001050902,0.00008440619,0.001013088],"category_scores_gemma":[0.0003150705,0.0003333818,0.00006095002,0.0007871343,0.0002168689,0.0006575194,0.002218262,0.0005959697,0.002209306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002692692,"about_ca_system_score_gemma":0.0000270121,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02793854,"about_ca_topic_score_gemma":0.005543886,"domain_scores_codex":[0.9956307,0.0005899173,0.0005643484,0.001596887,0.0008589888,0.0007590915],"domain_scores_gemma":[0.9969649,0.001181922,0.0002449266,0.001303593,0.00001329108,0.0002913953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004763397,0.0002101769,0.8941162,0.000001196675,0.002924272,0.00004826341,0.008631331,0.0005556225,0.0004407172,1.689659e-7,0.004703961,0.08789182],"study_design_scores_gemma":[0.001245053,0.0005621988,0.9756238,0.00007311704,0.00415134,9.959226e-7,0.002989492,0.004949552,0.001040793,0.00001606442,0.008941487,0.0004060893],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912327,0.002069989,0.00006200491,0.001444421,0.0002713544,0.001703331,0.002766285,0.0001886456,0.0002613007],"genre_scores_gemma":[0.9949739,0.0007552539,0.0003995186,0.001046752,0.0001325487,0.0001341576,0.002465599,0.0000625163,0.00002973087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08748572,"threshold_uncertainty_score":0.9999118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05879846385186262,"score_gpt":0.2820846960651928,"score_spread":0.2232862322133301,"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."}}