The influence of cholinesterase inhibitor therapy for dementia on risk of cardiac pacemaker insertion: a retrospective, population-based, health administrative databases study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Cholinesterase inhibitors are used to treat the symptoms of dementia and can theoretically cause bradycardia. Previous studies suggest that patients taking these medications have an increased risk of undergoing pacemaker insertion. Since these drugs have a marginal impact on patient outcomes, it might be preferable to change drug treatment rather than implant a pacemaker. This population-based study determined the association of people with dementia exposed to cholinesterase inhibitor medication and pacemaker insertion. METHODS: We used data from the Ontario health administrative databases from January 1, 1993 to June 30, 2012. We included all community-dwelling seniors who had a code for dementia and were exposed to cholinesterase inhibitors (donezepil, galantamine, and rivastigmine) and/or drugs used to treat co-morbidities of hypertension, diabetes, depression and hypothyroidism. We controlled for exposure to anti-arrhythmic drugs. Observation started at first exposure to any medication and continued until the earliest of pacemaker insertion, death, or end of study. RESULTS: 2,353,909 people were included with 96,000 (4.1%) undergoing pacemaker insertion during the observation period. Case-control analysis showed that pacemaker patients were less likely to be coded with dementia (unadjusted OR 0.42 [95%CI 0.41-0.42]) or exposed to cholinesterase inhibitors (unadjusted OR 0.39 [95%CI 0.37-0.41]). That Cohort analysis showed patients with dementia taking cholinesterase inhibitors had a decreased risk of pacemaker insertion (unadj-HR 0.58 [0.55-0.61]). Adjustment for patient age, sex, and other medications did not notably change results, as did restricting the analysis to incident users. CONCLUSIONS: Patients taking cholinesterase inhibitors rarely undergo, and have a significantly reduced risk of, cardiac pacemaker insertion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".