Allostatic load but not medical burden predicts memory performance in late‐life bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Older patients with bipolar disorder (BD) present with variable degrees of cognitive impairment. Over time, stress, mood episodes, and comorbidities increase the body's allostatic load. We assessed the extent to which allostatic load vs more traditional measures of medical burden account for the heterogeneity in cognition in this population. METHODS: Thirty-five older euthymic patients with BD and 30 age-equated, gender-equated, and education-equated comparison participants were administered a comprehensive assessment including a neuropsychological battery, and 9 physiological measures to determine allostatic load. The relationship among allostatic load, medical burden, and cognition was assessed. RESULTS: Compared with the mentally healthy comparators, patients were impaired globally, and in 4 cognitive domains-information-processing speed / executive functioning, delayed memory, language, and visuomotor ability, and presented with greater medical burden but not a different allostatic load. Allostatic load, but not medical burden, was associated with delayed memory performance both in a correlational analysis and in a multivariate regression analysis. CONCLUSION: Euthymic older patients with BD are impaired on several cognitive domains and have high medical burden. Their memory performance is more strongly associated with allostatic load than with traditional measures of medical burden. These findings need to be replicated and extended longitudinally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".