Do Antidepressants Lower the Prevalence of Lithium-Associated Hypernatremia and Symptomatic Polyuria in the Elderly?
Bibliographic record
Abstract
BACKGROUND: Clinically important measures of lithium-induced nephrogenic diabetes insipidus (NDI) such as hypernatremia have not been well-studied. This is especially relevant for the elderly who, in comparison to younger adults, may become symptomatic and require hospitalization with relatively small elevations in sodium levels. We hypothesized that antidepressant use, which has been associated with the syndrome of inappropriate antidiuretic hormone secretion, has a protective effect against lithium-associated hypernatremia in the elderly. METHODS: Retrospective cohort study of 55 geriatric psychiatry outpatients followed at tertiary-care hospitals. Patients using lithium and antidepressants were compared with those using lithium alone for prevalence rates of hypernatremia during a 15-year observational period. RESULTS: The prevalence of hypernatremia was less in patients who had concurrent use of lithium and antidepressants, as compared to lithium alone 3/35 (8.6%) vs. 8/20 (40%), OR 0.14, p = .011. CONCLUSIONS: Our results suggest that elderly lithium patients are less likely to develop hypernatremia if they are taking antidepressants concurrently. Whether antidepressants may be useful in the prevention of lithium-associated hypernatremia should be assessed in future prospective observational or treatment studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".