Clinical and methodological considerations for psychological treatment of cognitive impairment in major depressive disorder
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is considered a core feature of major depressive disorder (MDD) and research into psychological treatments aiming to address cognitive impairment are gaining momentum. Compared with the well-established research base of cognitive treatment trials in schizophrenia, including meta-analyses, mood disorder research is much more preliminary. AIMS: To focus on identifying the important factors to consider in developing larger-scale psychological treatment trials targeting cognitive impairment in mood disorders. Trial design recommendations have been published for cognitive treatment trials in bipolar disorder. METHOD: An in-depth discussion of methodological considerations in the development of cognitive treatment trials for MDD. RESULTS: Methodological considerations include: screening for, and defining, cognitive impairment; mood state when cognitive intervention begins; medication monitoring during cognitive interventions; use of concomitant therapy; level of therapist involvement; duration and dose of treatment; choice of specific cognitive training exercises; home practice; improving adherence; appropriate comparison therapies in clinical trials; and choice of primary outcomes. CONCLUSIONS: As well as guidance for clinical trial development, this review may be helpful for clinicians wanting to provide cognitive interventions for individuals with MDD.
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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 | 0.594 | 0.740 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| 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".