Bibliographic record
Abstract
PURPOSE OF REVIEW: Cognitive dysfunction in major depressive disorder (MDD) is common, pervasive across multiple subdomains of cognitive function, and is a principle determinant of health outcomes from patient, provider, and societal perspectives. The overarching aim herein is to provide rationale for the evaluation, measurement, and specific treatment of cognitive function in adults with MDD. RECENT FINDINGS: Evidence indicates that cognitive dysfunction in MDD is a critical mediator of workplace disability. Systematic evaluation and measurement of cognitive function is warranted. All individuals with MDD should be evaluated for both objective and subjective cognitive dysfunction. Although differences between antidepressants in overall antidepressant efficacy are not consistent, unequivocal differences in improving measures of cognitive function are noted with evidence indicating that vortioxetine has multidomain cognitive benefits, whereas duloxetine has replicated evidence of improving measures of acquisition and recall (i.e. memory). SUMMARY: The probability of functional recovery in MDD is likely to increase with interventions that specifically target and improve measures of cognitive function. Clinicians are encouraged to evaluate patients using a validated measure (e.g. the THINC-it tool); prevention of cognitive impairment in MDD is a critical therapeutic priority. Vortioxetine and duloxetine benefit measures of cognitive function in MDD. Preliminary evidence of beneficial effects on cognitive emotional processing are reported with ketamine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".