Meta‐analysis of the association between cognitive abilities and everyday functioning in bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Neurocognitive deficits are common in bipolar disorder and contribute to functional disability. However, the degree to which general and specific cognitive deficits affect everyday functioning in bipolar disorder is unknown. The goal of this meta-analysis was to examine the magnitude of the effect of specific neurocognitive abilities on everyday functioning in bipolar disorder. METHODS: We conducted a comprehensive meta-analysis of studies that reported associations between performance on objective neuropsychological tasks and everyday functioning among individuals with bipolar disorder. From an initial pool of 486 papers, 22 studies met inclusion criteria, comprising a total of 1344 participants. Correlation coefficients were calculated for 11 cognitive domains and four measurement modalities for functioning. We also examined effect moderators, such as sample age, clinical state, and study design. RESULTS: The mean Pearson correlation between neurocognitive ability and functioning was 0.27, and was significant for all cognitive domains and varied little by cognitive domain. Correlations varied by methods of everyday functioning assessment, being lower for clinician and self-report than performance-based tasks and real-world milestones such as employment. None of the moderator analyses were significant. CONCLUSIONS: Overall, the strength of association between cognitive ability and everyday functioning in bipolar disorder is strikingly similar to that seen in schizophrenia, with little evidence for differences across cognitive domains. The strength of association differed to a greater extent according to functional measurement approach.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".