{"id":"W4206272743","doi":"10.2196/28333","title":"Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Social Fund; National Institute on Aging; National Institutes of Health; Mind and Life Institute","keywords":"Overfitting; Computer science; Machine learning; Artificial intelligence; Gradient boosting; Working memory; Context (archaeology); Natural language processing; Psychology; Cognition; Random forest; Cognitive psychology; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000748093,0.0001145285,0.0002116133,0.0002977462,0.0005190565,0.00005350611,0.00005671028,0.00003195532,0.0001716363],"category_scores_gemma":[0.00004238998,0.0001194876,0.00003973199,0.0003188217,0.00003413598,0.0002163327,0.0002200536,0.0006797356,0.000001653973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001814714,"about_ca_system_score_gemma":0.0001217062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007965682,"about_ca_topic_score_gemma":0.00001929701,"domain_scores_codex":[0.998527,0.0001829464,0.0002902656,0.0001931744,0.0004474669,0.0003591374],"domain_scores_gemma":[0.9996393,0.0000488779,0.0001156134,0.00006763934,0.00003766516,0.00009095457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008078474,0.0001503235,0.8367754,0.0006030105,0.00003873721,0.00005855306,0.07858755,0.00009689893,0.0000970826,0.0000261,0.0000352513,0.08272326],"study_design_scores_gemma":[0.01112346,0.0003863448,0.4254107,0.0004286155,0.00004400338,0.0001581846,0.287561,0.2737563,0.00003141163,0.000002042375,0.0008095345,0.0002882935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929078,0.0003917,0.0001930028,0.0004422404,0.00004094463,0.0007713705,0.000003685245,0.00007333914,0.005175966],"genre_scores_gemma":[0.9984518,0.00001179722,0.0001301779,0.000903642,0.0001267414,0.0001505098,0.0001010907,0.00001675407,0.000107476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4113646,"threshold_uncertainty_score":0.4872565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194359494520855,"score_gpt":0.3208368303819538,"score_spread":0.2988932354367452,"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."}}