Domain-specific cognitive function in euthymic bipolar disorder: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Euthymic bipolar disorder (BD) is associated with general and domain-specific cognitive impairment, which predicts poor occupational and social functioning. Methods We searched Embase, Medline, and PsycInfo for articles published between database inception and June 2024, examining cognitive domains in euthymic BD. We conducted meta-analysis, meta-regressions, including premorbid IQ, demographic, and clinical variables. Newcastle Ottawa Scale, I 2 statistic, and funnel plots/Egger’s and Begg’s Test were used to assess quality, heterogeneity, and publication bias, respectively. The Benjamini-Hochberg (BH) procedure was utilised for multiple comparisons. Results We identified 95 groups from 75 studies ( N = 4,404 BD & 4,037 HC). BD showed significant impairment in general cognitive functioning (Hedge’s g = −0.58, 95%CI: −0.79, −0.37, p <.01), verbal memory (Hedge’s g = −0.70, 95%CI: −0.79, −0.60, p <.01), executive function (Hedge’s g = −0.69, 95%CI: −0.78, −0.60, p <.01), visuo-spatial memory (Hedge’s g = −0.68, 95%CI: −0.83, −0.53, p <.01), attention/processing speed (Hedge’s g = −0.64, 95%CI: −0.75, −0.54, p <.01), working memory (Hedge’s g = −0.61, 95%CI: −0.74, −0.49, p <.01), and premorbid IQ (Hedge’s g = −0.24, 95%CI: −0.36, −0.12, p <.01). Demographic and clinical factors were not associated with cognitive performance, except for a statistically significant, but small positive correlation between years of education and lower impairment in verbal memory, β = .066, adjusted p <.05. Conclusions Our findings highlight cognitive domains impaired in euthymic BD, indicating targets for interventions. Substantial variance is unexplained, warranting focus on larger samples of individual-level data.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".