Aging changes and medical complexity in late-life bipolar disorder: emerging research findings that may help advance care
Bibliographic record
Abstract
Demographic trends globally point in the direction of increasing numbers of older people with serious and chronic mental disorders, such as bipolar disorder (BD). While there has been growing sophistication and understanding in treatments for BD generally, data specific to older people with BD are limited. Recent reviews, secondary analyses and some new research confirm complexity and aging-related issues relevant to later-life BD. Confounding variables that must be considered when studying older BD individuals include clinical heterogeneity, medical comorbidity, cognitive impairment and concomitant psychotropic medication. This article will review current and emerging data on aging- and disease-related issues that complicate assessment and treatment of older individuals with BD. We will discuss common comorbid medical conditions that affect BD elders, how aging may affect cognition and treatment, including the effects of lithium and other psychotropic drugs on the aging brain, and recent research using neuroimaging techniques that may shed light on understanding the mechanisms of illness progression and on treatment response. Finally, we will discuss implications for future work in geriatric BD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".