Serotonin reuptake inhibition for the prevention of vasovagal syncope: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose: Vasovagal syncope is a common clinical condition that lacks effective medical therapies despite being associated with significant morbidity. Current guidelines suggest that serotonin-specific reuptake inhibitors, might suppress vasovagal syncope but supporting studies have been small and heterogenous. The purpose was to evaluate the efficacy of serotonin-specific reuptake inhibitors to prevent syncope in patients with recurrent vasovagal syncope by conducting a systematic review and meta-analysis of published studies. Methods: Relevant randomized controlled trials were identified from the MEDLINE and Embase databases without language restriction from inception to August 2022, and clinicaltrials.gov. All studies were conducted in clinical syncope populations and compared the benefit of serotonin versus placebo. Weighted relative risks were estimated using random effects meta-analysis techniques. Results: Three studies (n=204) met inclusion criteria. Patients were 42±13 years of age and 51% female. Serotonin-specific reuptake inhibitors were found to substantially reduce the likelihood of a patient having at least one recurrence of vasovagal syncope (relative risk [RR]=0.34 (0.20-0.60), p<0.01) with minimal between-study heterogeneity (I2=0%, p = 0.67). Serotonin-specific reuptake inhibitors in two reports provided significant protection against clinical presyncope (RR = 0.43 (0.24-0.77, p <0.01), with minimal between-study heterogeneity (I2 = 0%, p = 0.80). Conclusions: Serotonin-specific reuptake inhibitors may be effective in preventing syncope induced by head-up-tilt testing and in syncope in the community in randomized, double-blinded clinical trials.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".