{"id":"W4396886468","doi":"10.1371/journal.pone.0302902","title":"Machine learning for predicting cognitive deficits using auditory and demographic factors","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Deafness and Other Communication Disorders; National Cancer Institute; National Institutes of Health","keywords":"Neurocognitive; Medicine; Cognition; Cognitive test; Verbal learning; Audiology; Clinical psychology; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005887252,0.001155261,0.0008198094,0.002504716,0.0003693771,0.001154332,0.0007034332,0.001081881,0.001254243],"category_scores_gemma":[0.01710326,0.000249833,0.0009860952,0.00125176,0.0003995483,0.000905698,0.0005518539,0.001578698,0.0006317908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006000941,"about_ca_system_score_gemma":0.00105152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005216885,"about_ca_topic_score_gemma":0.003581128,"domain_scores_codex":[0.9988195,0.0006095749,0.0001145136,0.0002089009,0.0001614578,0.00008599003],"domain_scores_gemma":[0.9879794,0.0100522,0.0007317464,0.0003067088,0.0007614976,0.0001684701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009427138,0.00111479,0.2938391,0.0003621435,0.0007156224,0.0002112368,0.0001637945,0.3404675,0.001780545,0.001252727,0.005551691,0.3535982],"study_design_scores_gemma":[0.00005399307,0.0003662541,0.02377177,0.0001314245,0.0001122531,0.0001141924,0.00009429742,0.9689797,0.001006446,0.004624458,0.0007067297,0.00003853849],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6281998,0.006194857,0.3503631,0.003904565,0.0003470835,0.0007474184,0.003706463,0.001881496,0.004655113],"genre_scores_gemma":[0.9351691,0.0009574732,0.06079954,0.0002863973,0.0001698289,0.0002857888,0.001442323,0.00002330952,0.0008663639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005887252,"threshold_uncertainty_score":0.03113514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06561011449878251,"score_gpt":0.3088138410716083,"score_spread":0.2432037265728258,"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."}}