Residual Cognitive Deficit in Adults with Depression who Recovered after 6-month Treatment: Stable versus State-Dependent Markers
Bibliographic record
Abstract
BACKGROUND: Knowledge of depression-related disturbances in cognitive functioning is advancing, but little is known about the cognitive response to treatment for major depression, especially in younger adults. This study investigated the deficits in multiple cognitive domains in middle-aged patients with major depressive disorder (MDD), using a prospective follow-up study design. METHODS: The sample consisted of 13 medication-free MDD patients and 13 education- and age-matched healthy controls. All subjects were administered clinical measures as well as a comprehensive neurocognitive test battery aimed at assessing multiple cognitive domains at the time of recruitment. Patients remitted after 6 months following treatment repeated the neurocognitive assessment. RESULTS: There were significant differences between the depressed subjects and controls at baseline. MDD patients with remitted symptoms still showed significant deficits in executive function and motor function, but not in memory or attention domains. Patients had significant improvement in memory and attention domains only, once their depressive symptoms had subsided; while executive functioning as well as motor functioning remained unchanged. CONCLUSIONS: Executive functioning and motor functioning deficits might be stable vulnerability indicators for MDD, and memory and attention impairment might serve as state-dependent indicators for MDD. KEYWORDS: Major depressive disorder; Remission; Residual; Cognitive deficits; Follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".