{"id":"W4415525005","doi":"10.1371/journal.pone.0334704","title":"Screening mild cognitive impairment using aspects of personal, social, and functional lifestyle: Machine Learning Approaches","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Interpretability; Psychosocial; Logistic regression; Cognition; Medical diagnosis; Intervention (counseling); MEDLINE","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":[0.004131009,0.001250559,0.0006981132,0.002733387,0.0003215086,0.001353402,0.0007936176,0.000739172,0.0008983026],"category_scores_gemma":[0.008466665,0.0002377894,0.001100993,0.001553825,0.0003243375,0.0007975363,0.0006792222,0.001223277,0.0004405317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005885754,"about_ca_system_score_gemma":0.0007518571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005987979,"about_ca_topic_score_gemma":0.007649181,"domain_scores_codex":[0.9989636,0.0005397827,0.00007181382,0.0002318983,0.0001291275,0.00006377824],"domain_scores_gemma":[0.9969458,0.002070748,0.000358993,0.0001641877,0.0003476664,0.0001126123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004451236,0.001064762,0.4119736,0.000364552,0.001229412,0.0001973233,0.0002850381,0.1757149,0.001664766,0.0009710866,0.003852057,0.4022375],"study_design_scores_gemma":[0.00004300018,0.0005798277,0.1189797,0.0003977127,0.0003339525,0.0002129418,0.0002866634,0.8671884,0.001233888,0.008727624,0.00194866,0.00006760899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7927296,0.008659469,0.1849823,0.004385628,0.0001707903,0.0003838851,0.002847907,0.0009317539,0.004908734],"genre_scores_gemma":[0.9589362,0.001063233,0.03773058,0.000240919,0.00009957084,0.0001311395,0.001072478,0.00001719668,0.0007087389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005987979,"threshold_uncertainty_score":0.02184713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.144609491494399,"score_gpt":0.3146753937413195,"score_spread":0.1700659022469205,"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."}}