Successful computer-assisted cognitive remediation therapy in patients with unipolar depression: a proof of principle study
Bibliographic record
Abstract
BACKGROUND: Despite increasing awareness of the extent and severity of cognitive deficits in major depressive disorder (MDD), trials of cognitive remediation have not been conducted. We conducted a 10-week course of cognitive remediation in patients with long-term MDD to probe whether deficits in four targeted cognitive domains, (i) memory, (ii) attention, (iii) executive functioning and (iv) psychomotor speed, could be improved by this intervention. METHOD: We administered a computerized cognitive retraining package (PSSCogReHab) with demonstrated efficacy to 12 stable patients with recurrent MDD. Twelve matched patients with MDD and a group of healthy control participants were included for comparison; neither comparator group received the intervention that involved stimulation of cognitive functions through targeted, repetitive exercises in each domain. RESULTS: Patients who received cognitive training improved on a range of neuropsychological tests targeting attention, verbal learning and memory, psychomotor speed and executive function. This improvement exceeded that observed over the same time period in a group of matched comparisons. There was no change in depressive symptom scores over the course of the trial, thus improvement in cognitive performance occurred independent of other illness variables. CONCLUSIONS: These results provide preliminary evidence that improvement of cognitive functions through targeted, repetitive exercises is a viable method of cognitive remediation in patients with recurrent 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".