The Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE‐BD) project: Understanding older‐age bipolar disorder by combining multiple datasets
Bibliographic record
Abstract
OBJECTIVE: There is a dearth of research about the aging process among individuals with bipolar disorder (BD). One potential strategy to overcome the challenge of interpreting findings from existing limited older-age bipolar disorder (OABD) research studies is to pool or integrate data, taking advantage of potential overlap or similarities in assessment methods and harmonizing or cross-walking measurements where different measurement tools are used to evaluate overlapping construct domains. This report describes the methods and initial start-up activities of a first-ever initiative to create an integrated OABD-focused database, the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) project. METHODS: Building on preliminary work conducted by members of the International Society for Bipolar Disorders OABD taskforce, the GAGE-BD project will be operationalized in four stages intended to ready the dataset for hypothesis-driven analyses, establish a consortium of investigators to guide exploration, and set the stage for prospective investigation using a common dataset that will facilitate a high degree of generalizability. RESULTS: Initial efforts in GAGE-BD have brought together 14 international investigators representing a broad geographic distribution and data on over 1,000 OABD. Start-up efforts include communication and guidance on meeting regulatory requirements, establishing a Steering Committee to guide an incremental analysis strategy, and learning from existing multisite data collaborations and other support resources. DISCUSSION: The GAGE-BD project aims to advance understanding of associations between age, BD symptoms, medical burden, cognition and functioning across the life span and set the stage for future prospective research that can advance the understanding of OABD.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".