Assessing bipolar disorder in the older adult: the GERI‐BD toolbox
Bibliographic record
Abstract
INTRODUCTION: The evidence base regarding characteristics of older adults with bipolar disorder (BD) remains limited. The NIH-funded multicenter study Acute Pharmacotherapy of Late-Life Mania (GERI-BD) assessed various clinical domains before and during mood stabilizer treatment in older adults participating in a 9-week, double-blind randomized controlled trial. We describe the rationale for selecting these instruments. METHODS: Domains and instruments were selected on the basis of the study design and the participants. The investigators' experience in clinical trials involving young adults with BD or older adults with major depressive disorder, along with open studies of older adults with BD, contributed to the selection process. RESULTS: We identified domains and selected instruments that could be used to assess the participants given their diagnostic, treatment history, and medical and mood state characteristics. They were also intended to measure tolerability and efficacy and permit examination of potential moderating and mediating factors. CONCLUSIONS: Decisions regarding the assessment domains to be included in the clinical trial highlight the challenges facing researchers studying drug treatments for older adults with BD, or more generally, mood disorders. We suggest that the domains and instruments selected by GERI-BD investigators constitute a "toolbox" that can be customized for other investigators. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".