Anticonvulsant Use in Older Age Bipolar Disorder in a Global Sample from the Global Aging and Geriatric Experiments in Bipolar Disorder Project: Utilisation d’anticonvulsivants pour le traitement des troubles bipolaires du sujet âgé auprès d’un échantillon mondial provenant du projet GAGE-BD
Bibliographic record
Abstract
BACKGROUND: Anticonvulsants are an essential treatment for bipolar disorder; however, there is relatively little known about their use in older age bipolar disorder (OABD). In this paper, which leverages a large international dataset, we aim to 1) describe the use of anticonvulsants in OABD compared to younger age bipolar disorder (YABD; ages <50 years old) and 2) explore any demographic/clinical correlates. METHODS: A secondary analysis was conducted on the international data from the Global Aging and Geriatric Experiments in Bipolar Disorder project. The main objective was to report the prevalence of anticonvulsant use in OABD over 50 years old (mean age = 62.27) and the most prescribed anticonvulsant. Additional analysis explored any demographic and clinical correlates associated with anticonvulsant use. Generalized linear mixed models were used for this analysis. RESULTS: Of the 2,691 participants with bipolar disorder who had anticonvulsant prescribing data, 34.4% (n = 926) used anticonvulsants at the time of study. Rates of anticonvulsant prescribing did not significantly differ between OABD and YABD groups (36.7% (n = 666) vs. 29.7% (n = 260)). Anticonvulsant prescribing patterns for OABD and YABD did not significantly differ, with valproate as the most prescribed anticonvulsant. OABD anticonvulsant users had less lithium use, more antidepressant use, more rapid cycling, more mood episodes and more cardiovascular comorbidities compared to nonusers. CONCLUSION: Anticonvulsant use was similar in OABD and YABD. A number of important clinical correlates of anticonvulsant use were identified.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".