Executive Function Predicts Antidepressant Treatment Noncompletion in Late-Life Depression
Bibliographic record
Abstract
OBJECTIVE: To examine whether executive function (EF) is associated with nonremission and noncompletion of antidepressant pharmacotherapy in older adults with depression. DESIGN: In this prospective study (July 2009 to May 2014), older adults (aged ≥ 60 years; n = 468) with a DSM-IV-defined major depressive episode diagnosed via structured interview received 12 weeks of venlafaxine extended release with the goal of achieving remission. A hypothesis was made that worse baseline EF would predict both nonremission and noncompletion (primary outcomes). Treatment-related factors, including side effects and nonadherence, were also studied. METHODS: Baseline EF, including response inhibition and set-shifting, was assessed with subtests of the Delis-Kaplan Executive Function System and the semantic fluency subtest of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Attention, immediate memory, delayed memory, visuospatial ability, and global cognition were also assessed with the RBANS. RESULTS: Of 468 participants, 96 (21%) failed to complete the treatment trial, 191 (41%) completed and remitted, and 181 (39%) completed and did not remit. Univariate analyses indicated that some EFs (set-shifting and semantic fluency) and other cognitive variables (attention, immediate memory, visuospatial ability, and global cognition) predicted treatment noncompletion, whereas no cognitive variables predicted nonremission. In a multivariate logistic regression model, semantic fluency (P = .003), comorbid medical burden (P < .001), and early nonadherence (P < .001) were significant predictors of treatment noncompletion. CONCLUSIONS: Poorer EF predicted treatment noncompletion. These findings suggest that EFs of initiation and set maintenance (examined by the semantic fluency task) may allow depressed elderly individuals to engage and stay in treatment. Identification of those at risk for noncompletion may help implementation strategies for personalized care. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00892047.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".