Bipolar I and bipolar <scp>II</scp> subtypes in older age: Results from the Global Aging and Geriatric Experiments in Bipolar Disorder (<scp>GAGE‐BD</scp>) project
Bibliographic record
Abstract
OBJECTIVES: The distinction between bipolar I disorder (BD-I) and bipolar II disorder (BD-II) has been a topic of long-lasting debate. This study examined differences between BD-I and BD-II in a large, global sample of OABD, focusing on general functioning, cognition and somatic burden as these domains are often affected in OABD. METHODS: Cross-sectional analyses were conducted with data from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) database. The sample included 963 participants aged ≥50 years (714 BD-I, 249 BD-II). Sociodemographic and clinical factors were compared between BD subtypes including adjustment for study cohort. Multivariable analyses were conducted with generalized linear mixed models (GLMMs) and estimated associations between BD subtype and (1) general functioning (GAF), (2) cognitive performance (g-score) and (3) somatic burden, with study cohort as random intercept. RESULTS: After adjustment for study cohort, BD-II patients more often had a late onset ≥50 years (p = 0.008) and more current severe depression (p = 0.041). BD-I patients were more likely to have a history of psychiatric hospitalization (p < 0.001) and current use of anti-psychotics (p = 0.003). Multivariable analyses showed that BD subtype was not related to GAF, cognitive g-score or somatic burden. CONCLUSION: BD-I and BD-II patients did not differ in terms of general functioning, cognitive impairment or somatic burden. Some clinical differences were observed between the groups, which could be the consequence of diagnostic definitions. The distinction between BD-I and BD-II is not the best way to subtype OABD patients. Future research should investigate other disease specifiers in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".