The effectiveness and limitations of regulatory warnings for the safe prescribing of citalopram
Bibliographic record
Abstract
BACKGROUND: Citalopram is the most commonly prescribed antidepressant in Canada. Concerns have been raised about its cardiac safety, and a dose-dependent prolongation of the QT interval has been documented. Drug interactions involving concomitant use of other medications that prolong the QT interval or increase citalopram levels by interfering with its metabolism increase the cardiac risk. Regulatory bodies (Health Canada and the US Food and Drug Administration) issued warnings and required labeling changes in 2011/2012, suggesting maximum citalopram doses (<40 mg for those <65 years; <20 mg for those ≥65 years) and avoiding drug interactions that increase cardiac risk. The purpose of this study is to assess the impact of these warnings on citalopram prescribing practices. METHODS: A quasi-experimental interrupted time series analysis was conducted using all citalopram prescribing data from the population of Manitoba, Canada from 1999 to 2014. This allowed for the examination of high-dose prescribing (above regulatory warning levels) and the number of interacting medications per citalopram prescription. RESULTS: There was a dramatic decline in the prescribing of high doses in both age groups, with a 64.8% decline in those <65 years and 33.6% in those ≥65 years. Segmented regression models indicated significant breakpoints in the third quarter of 2011 for both age groups (P<0.0001), corresponding to the time the regulatory warnings were issued. There appeared to be no impact of the warnings on the prescribing of interacting medications. The number of interacting medications actually increased in the postwarning period (<65, 0.78-0.81 interactions per citalopram prescription; ≥65, 0.93-0.94, P<0.001). CONCLUSION: Regulatory changes appear to have produced an important reduction in the high-dose prescribing of citalopram. In contrast to this relatively simple dosage change, there was no indication that the more complex issue of resolving drug-drug interactions was impacted by regulatory warnings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".