COGNITIVE DEFICITS AND FUNCTIONAL OUTCOMES IN MAJOR DEPRESSIVE DISORDER: DETERMINANTS, SUBSTRATES, AND TREATMENT INTERVENTIONS
Bibliographic record
Abstract
BACKGROUND: Few reports have aimed to describe the mediational effect of cognitive deficits on functional outcomes in major depressive disorder (MDD), and relatively few interventions are demonstrated to mitigate cognitive deficits in MDD. METHODS: Studies enrolling subjects between the ages of 18-65 were selected for review. Bibliographies from identified articles were reviewed to identify additional original reports aligned with our objectives. RESULTS: Cognitive deficits in MDD are consistent, replicable, nonspecific, and clinically significant. The aggregated estimated effect size of cognitive deficits in MDD is small to medium. Pronounced deficits in executive function (≥1 SD below the normative mean) are evident in ∼20-30% of individuals with MDD). Other replicated abnormalities are in the domains of working memory, attention, and psychomotor processing speed. Mediational studies indicate that cognitive deficits may account for the largest percentage of variance with respect to the link between psychosocial dysfunction (notably workforce performance) and MDD. No conventional antidepressant has been sufficiently studied and/or demonstrated robust procognitive effects in MDD. CONCLUSIONS: Cognitive deficits in MDD are a principal mediator of psychosocial impairment, notably workforce performance. The hazards posed by cognitive deficits in MDD underscore the need to identify a consensus-based neurocognitive battery for research and clinical purposes. Interventions (pharmacological, behavioral, neuromodulatory) that engage multiple physiological systems implicated in cognitive deficits hold promise to reduce, reverse, and prevent cognitive deficits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".