Cognitive impairment in major depressive disorder
Bibliographic record
Abstract
Cognitive dysfunction is a symptomatic domain identified across many mental disorders. Cognitive deficits in individuals with major depressive disorder (MDD) contribute significantly to occupational and functional disability. Notably, cognitive subdomains such as learning and memory, executive functioning, processing speed, and attention and concentration are significantly impaired during, and between, episodes in individuals with MDD. Most antidepressants have not been developed and/or evaluated for their ability to directly and independently ameliorate cognitive deficits. Multiple interacting neurobiological mechanisms (eg, neuroinflammation) are implicated as subserving cognitive deficits in MDD. A testable hypothesis, with preliminary support, posits that improving performance across cognitive domains in individuals with MDD may improve psychosocial function, workplace function, quality of life, and other patient-reported outcomes, independent of effects on core mood symptoms. Herein we aim to (1) provide a rationale for prioritizing cognitive deficits as a therapeutic target, (2) briefly discuss the neurobiological substrates subserving cognitive dysfunction, and (3) provide an update on current and future treatment avenues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".