{"id":"W4392507767","doi":"10.3389/fdgth.2024.1265846","title":"Multidimensional digital biomarker phenotypes for mild cognitive impairment: considerations for early identification, diagnosis and monitoring","year":2024,"lang":"en","type":"article","venue":"Frontiers in Digital Health","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Government of Alberta","keywords":"Biomarker; Identification (biology); Population; Computer science; Cognition; Medicine; Psychiatry; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000293155,0.0001658271,0.0002631499,0.0003335704,0.0001912013,0.0004193602,0.00003134544,0.00005798444,0.000009512704],"category_scores_gemma":[0.0004478623,0.0001561238,0.0001019705,0.0002006182,0.0001305111,0.0006920123,0.00003649557,0.0001078806,0.000008451858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001648677,"about_ca_system_score_gemma":0.0003182458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001338784,"about_ca_topic_score_gemma":0.000002138361,"domain_scores_codex":[0.9984531,0.00001505897,0.0004052911,0.0004491772,0.0002504792,0.0004268996],"domain_scores_gemma":[0.9987231,0.0006597891,0.0000513673,0.00009008178,0.0002494139,0.0002261803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005803818,0.0005317996,0.8415253,0.001002011,0.0002704085,0.00001503168,0.0008605692,3.935288e-7,0.00001757141,0.0003270349,0.01951339,0.1353561],"study_design_scores_gemma":[0.005238056,0.001699602,0.9679877,0.001812649,0.00009936696,0.0000236145,0.002426822,0.002547387,0.001035031,0.01342682,0.003360114,0.0003428246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570614,0.00598884,0.02673117,0.002749351,0.0008921122,0.00449548,0.00172699,0.0001034316,0.0002512051],"genre_scores_gemma":[0.9941891,0.0002083565,0.002947384,0.0001279729,0.0001261061,0.001184829,0.0003360191,0.00003474503,0.0008455313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1350133,"threshold_uncertainty_score":0.6366543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04010375472802818,"score_gpt":0.3575258662311606,"score_spread":0.3174221115031324,"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."}}