Impact of depressive episodes on cognitive deficits in early bipolar disorder: data from the Systematic Treatment Optimization Programme for Early Mania (STOP-EM)
Bibliographic record
Abstract
BACKGROUND: Although manic episodes reportedly contribute to cognitive deficits in bipolar I disorder, the contribution of depressive episodes is poorly researched. AIMS: We investigated the impact of depressive episodes on cognitive function early in the course of bipolar I disorder. METHOD: A total of 68 patients and 38 controls from the Systematic Treatment Optimization Programme for Early Mania (STOP-EM) first-episode mania programme were examined. We conducted (a) a cross-sectional analysis of the impact of prior depressive episodes on baseline cognitive function and (b) a prospective analysis assessing the contribution of depression recurrence within 1 year following a first episode of mania on cognitive functioning. RESULTS: The cross-sectional analysis showed no significant differences between patients with past depressive episodes compared with those without, on overall or individual domains of cognitive function (all P>0.09). The prospective analysis failed to reveal a significant group×time interaction for cognitive decline from baseline to 1 year (P = 0.99) in patients with a recurrence of depressive episodes compared with those with no recurrence. However, impaired verbal memory at baseline was associated with a depression recurrence within 1 year. CONCLUSIONS: Although deficits in all domains of cognitive function are seen in patients early in the course of bipolar disorder, depressive episodes do not confer additional burden on cognitive function. However, poorer verbal memory may serve as a marker for increased susceptibility to depression recurrence early in the course of illness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".