The Cognitive Profile of Older Adults With Treatment-Resistant Depression: An Analysis of the OPTIMUM Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: Major depressive disorder in older adults (late-life depression; LLD) is frequently associated with cognitive impairment, and some deficits (e.g., executive function) have been associated with a higher level of treatment resistance. However, the cognitive profile of treatment-resistant LLD (TR-LLD) has not been characterized. We hypothesized that patients with TR-LLD would show deficits in cognitive function, especially executive function, and that executive function deficits would predict poorer response to pharmacotherapy. DESIGN: Secondary analysis of baseline cognitive data from OPTIMUM, a multicenter RCT evaluating pharmacotherapy strategies for TR-LLD. SETTING: Five outpatient academic medical centers (4 US, 1 Canada). PARTICIPANTS: About 369 participants aged 60 and older from the OPTIMUM study. MEASUREMENTS: Baseline scores on individual tasks and composite scores from the NIH Toolbox-Cognition Battery were transformed into demographically-adjusted T-scores and compared to published norms. Impairments in the set shifting and inhibitory control tasks were investigated as predictors of depressive symptom change following treatment using ANCOVA models. RESULTS: Participants had low performance on tasks evaluating inhibitory control, processing speed, verbal/nonverbal memory, and the fluid composite, but normative performance on working memory and set shifting. Participants had high estimated premorbid IQ (superior Performance on oral reading recognition). Age and physical comorbidity negatively associated with processing speed. Impairments in set shifting predicted less improvement in depressive symptoms; impairments in inhibitory control did not. CONCLUSIONS: Participants with TR-LLD presented with broad cognitive deficits relative to healthy norms. Given poorer outcomes following standard pharmacotherapy associated with impaired set shifting, future research needs to identify alternative treatment strategies.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".