Executive Function Among Older Adults With Bipolar Disorder: A GAGE-BD Analysis
Bibliographic record
Abstract
OBJECTIVES: Executive function deficits in bipolar disorder (BD) are major contributors to disability in older age BD (OABD). We investigated the difference between OABD and age-equated healthy comparators (HC); and, in the OABD group, the associations of executive function with age, symptom severity, global cognition, and daily functioning. DESIGN: Cross-sectional analysis of executive function in OABD versus HC. SETTING: Analysis of large archival dataset harmonized from 12 international OABD studies. PARTICIPANTS: Older adults (≥50 years) with OABD (n = 614) and HC (n = 192). MEASUREMENTS: Executive function was assessed via Trail Making Test B (TMT-B) completion time; covariates included age, self-reported gender, education, study site, medications (antipsychotics, lithium), and psychomotor speed. RESULTS: Executive function was worse in OABD than in HC, even after controlling for psychomotor speed (p < 0.001). In the OABD group, test completion was associated with less severe manic symptoms (p < 0.001). Worse executive function was associated with older age (p = 0.001), antipsychotic use (p < 0.001), worse global cognition (p < 0.001), and worse daily functioning (p < 0.001). CONCLUSIONS: Executive dysfunction is a prominent feature of OABD, associated with several demographic and clinical characteristics. Future longitudinal studies of executive function and OABD need to assess the individual impact of impairment in executive function on everyday functioning to inform personalized interventions targeting specific patient subgroups.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".