Characterizing, Assessing, and Treating Cognitive Dysfunction in Major Depressive Disorder
Bibliographic record
Abstract
LEARNING OBJECTIVES: After participating in this activity, learners should be better able to:• Characterize cognitive dysfunction in patients with major depressive disorder.• Evaluate approaches to treating cognitive dysfunction in patients with major depressive disorder. ABSTRACT: Cognitive dysfunction is a core psychopathological domain in major depressive disorder (MDD) and is no longer considered to be a pseudo-specific phenomenon. Cognitive dysfunction in MDD is a principal determinant of patient-reported outcomes, which, hitherto, have been insufficiently targeted with existing multimodal treatments for MDD. The neural structures and substructures subserving cognitive function in MDD overlap with, yet are discrete from, those subserving emotion processing and affect regulation. Several modifiable factors influence the presence and extent of cognitive dysfunction in MDD, including clinical features (e.g., episode frequency and illness duration), comorbidity (e.g., obesity and diabetes), and iatrogenic artefact. Screening and measurement tools that comport with the clinical ecosystem are available to detect and measure cognitive function in MDD. Notwithstanding the availability of select antidepressants capable of exerting procognitive effects, most have not been sufficiently studied or rigorously evaluated. Promising pharmacological avenues, as well as psychosocial, behavioral, chronotherapeutic, and complementary alternative approaches, are currently being investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".