Measurement tools for assessment of older age bipolar disorder: A systematic review of the recent global literature
Bibliographic record
Abstract
OBJECTIVES: More than 50% of people with bipolar disorder will be age 60 years or older by 2030. There is a need for more data to guide assessment and treatment in older age bipolar disorder (OABD); however, interpretation of findings from small, single-site studies may not be generalizable and there are few large trials. As a step in the direction of coordinated large-scale OABD data collection, it is critical to identify which measurements are currently used and identify potential gaps in domains typically assessed. METHODS: An international group of OABD experts performed a systematic literature review to identify studies examining OABD in the past 6 years. Relevant articles were assessed to categorize the types of clinical, cognitive, biomarker, and neuroimaging OABD tools routinely used in OABD studies. RESULTS: A total of 53 papers were identified, with a broad range of assessments. Most studies evaluated demographic and clinical domains, with fewer studies assessing cognition. There are relatively few biomarker and neuroimaging data, and data collection methods were less comprehensively covered. CONCLUSION: Assessment tools used in the recent OABD literature may help to identify both a minimum and a comprehensive dataset that should be evaluated in OABD. Our review also highlights gaps where key clinical outcomes have not been routinely assessed. Biomarker and neuroimaging assessment could be further developed and standardized. Clinical data could be combined with neuroimaging, genetic, and other biomarkers in large-scale coordinated data collection to further improve our understanding of OABD phenomenology and biology, thereby contributing to research that advances care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".