Demographic and Clinical Characteristics of Lithium‐Treated Older Adults With Bipolar Disorder: A Replication Study From the Global Aging and Geriatric Experiments in Bipolar Disorder ( <scp>GAGE</scp> ‐ <scp>BD</scp> ) Project
Bibliographic record
Abstract
OBJECTIVES: To replicate a previous study addressing differences and similarities of older adults with bipolar disorder (OABD) treated with lithium as compared to those treated with other drugs. METHODS: Cross-sectional analysis of a harmonized dataset of the GAGE-BD Project, with over 2 thousand participants enrolled from two recruitment waves, with worldwide representation. Participants were allocated in two treatment groups according to the availability of information about current lithium use (Lithium, n = 754; Non-lithium, n = 1,161). We used linear regression, linear mixed and generalized linear mixed models to examine the associations between treatment groups and other variables, with emphasis on the distribution of socio-demographical and clinical variables, controlling for age, gender, and study site. RESULTS: We found statistical associations between lithium use and higher education level, as well as with bipolar-1 subtype, and a negative association with comorbid anxiety disorder. OABD patients treated with lithium had lower scores on depression rating scales, and better overall global cognitive and functional state. Lithium users also reported having fewer cardiovascular comorbidities. CONCLUSION: We found several potentially relevant differences in the clinical profile of OABD treated with lithium; nonetheless, the interpretation of the present results must take into account the methodological limitations inherent to the cross-sectional approach and data harmonization.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".