Antiepileptic drugs and hyponatremia in older adults: Two population‐based cohort studies
Bibliographic record
Abstract
OBJECTIVE: To examine the 30-day risk of hospitalization with hyponatremia associated with carbamazepine, valproic acid (V), phenytoin (P), or topiramate (T) use compared to nonuse in the outpatient setting among older adults. METHODS: We conducted two population-based, retrospective cohort studies in Ontario, Canada, between 2003 and 2015 using administrative health care databases of older adults. The first study compared carbamazepine users to a propensity-score matched group of antiepileptic drug nonusers, whereas the second compared V-P-T users to a propensity-score matched group of antiepileptic nonusers. The primary outcome was hospitalization with hyponatremia within 30 days of an antiepileptic prescription. RESULTS: The baseline characteristics between matched groups were similar in both cohorts. Carbamazepine use versus nonuse was associated with a higher 30-day risk of hospitalization with hyponatremia (82/21,191 [0.39%] versus 30/63,573 [0.05%]; relative risk [RR] 8.20, 95% confidence interval [CI] 5.40-12.46). Similarly, V-P-T use versus nonuse was associated with a higher 30-day risk of hospitalization with hyponatremia (34/20,155 [0.17%] versus 26/40,310 [0.06%]; RR 2.62, 95% CI 1.57-4.36). SIGNIFICANCE: Older adults prescribed carbamazepine and V-P-T have a higher risk of being hospitalized with hyponatremia compared to other adults with similar indicators of baseline health who were not prescribed antiepileptic drugs. Physicians should be mindful of this risk; when a patient presents to a hospital with symptomatic hyponatremia these drugs should be considered as potential causes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".