Cognitive remediation as a treatment for major depression: A rationale, review of evidence and recommendations for future research
Bibliographic record
Abstract
OBJECTIVE: There is considerable literature regarding the effectiveness of cognitive remediation (CR) in schizophrenia and in conditions such as stroke and traumatic brain injury. Patients with major depressive disorder (MDD) present with significant cognitive impairment which in many cases may not resolve with treatment. Neurobiological data suggest that this may relate to underlying dysfunction of pre-frontal cortical areas of the brain and their connections with limbic structures. There has been limited research into specific CR to activate these areas and target impaired cognitive function in MDD. We therefore review current evidence, examine the theoretical basis for and present a rationale for research into CR in MDD. In addition, we will examine important methodological issues in developing such an approach. METHOD: Based on preliminary studies using CR-based techniques, data from CR in schizophrenia, data regarding baseline and residual cognitive impairment in depression, and knowledge of the neurobiology of MDD, we examine the possible utility of CR strategies in the treatment of MDD and make recommendations for research in this area. RESULTS: A small number of previous studies have examined specific CR in MDD. The studies are small and inconclusive. However, data on the neuropsychological function and neurobiology of MDD suggest that this is an approach that deserves further attention and research. CONCLUSIONS: Further research is required in carefully selected populations, using well-defined CR techniques and some form of comparator treatment.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".