Degree of serotonin reuptake inhibition of antidepressants and ischemic risk
Bibliographic record
Abstract
OBJECTIVE: To assess whether use of antidepressants with strong inhibition of serotonin reuptake is associated with a decreased incidence of ischemic stroke and myocardial infarction (MI). METHODS: We conducted a cohort study using the UK Clinical Practice Research Datalink and considering new users of selective serotonin reuptake inhibitors (SSRIs) or third-generation antidepressants who were ≥18 years of age between 1995 and 2014. Using a nested case-control approach, we matched each case of a first ischemic stroke or MI identified during follow-up with up to 30 controls on age, sex, calendar time, and duration of follow-up. We estimated incidence rate ratios (RRs) and 95% confidence intervals (CIs) of each outcome associated with current use of strong compared with weak inhibitors of serotonin reuptake using conditional logistic regression. RESULTS: The cohort included 938,388 incident users of SSRIs (n = 868,755) or third-generation antidepressants (n = 69,633). Mean age at cohort entry was 46 years (64% women). During follow-up, 15,860 cases of ischemic stroke and 8,626 cases of MI were identified and matched to 473,712 and 258,022 controls, respectively. Compared with current use of weak inhibitors of serotonin reuptake, current use of strong inhibitors was associated with a decreased rate of ischemic stroke (RR 0.88, 95% CI 0.80-0.97), but the effect size was smaller in some sensitivity analyses. The rate of MI was similar between strong and weak inhibitors (RR 1.00, 95% CI 0.87-1.15). CONCLUSION: Our large population-based study suggests that antidepressants strongly inhibiting serotonin reuptake may be associated with a small decrease in the rate of ischemic stroke.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".