Effect of antidepressant use on admissions to hospital among elderly bipolar patients
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to examine the association between antidepressant use and hospitalization rate for mania or bipolar depression in a large, community-based sample of elderly bipolar patients. METHOD: Population-based retrospective cohort design. Administrative healthcare databases were linked for all individuals aged 66 years or older in the Province of Ontario, Canada. Bipolar subjects who received a prescription for an antidepressant medication (n = 1,072) between 1 April 1997 and 31 March 2001 comprised the antidepressant cohort. The control group (n = 3,000) consisted of randomly selected subjects from the eligible bipolar population who did not receive a prescription for an antidepressant medication during the same surveillance period. Primary outcomes were admission to hospital for a manic/mixed or depressive episode. RESULTS: During a total of 5135 person-years of follow up, 113 admissions for a manic/mixed episode and 28 admissions for a depressive episode were identified. Model based estimates adjusted for a number of covariates revealed that, as compared with the control group, the antidepressant cohort had significantly lower likelihood of admissions for manic/mixed (adjusted rate ratio [aRR] = 0.5, 95% CI = 0.3-0.8) but not depressive episodes (aRR = 0.7, 95% CI = 0.2-2.2). CONCLUSION: Antidepressant use among elderly bipolar patients was associated with decreased rates of hospitalization for manic/mixed episodes. This finding requires confirmation with further data of antidepressant use among elderly bipolars.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".