Serotonergic antidepressants and perioperative bleeding risk: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Serotonergic antidepressants (SAds) are known to increase bleeding events, with a number of recent studies investigating this risk in surgical settings. Our main objective was to synthesize the current evidence to evaluate the clinical importance of SAd-related bleeding risk in the perioperative period. METHODS: A systematic review of MEDLINE, Embase and PsychINFO until November 2013 was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. We examined the risk of perioperative bleeding adverse events in SAd users in comparison to controls. RESULTS: A total of 13 relevant studies were identified across a broad variety of surgical procedures. SAds were associated with an increased risk of perioperative bleeding (odds ratios [ORs] = 1.21 - 4.14) and blood transfusions (ORs = 0.93 - 3.71). CONCLUSIONS: SAds are associated with increased perioperative bleeding events, particularly abnormal bleeding and blood transfusions. From a clinical perspective, the potential bleeding risks of SAds in surgical settings need to be carefully weighed against their psychiatric benefits. Future research will need to investigate potential strategies to mitigate SAd-related bleeding risk in the surgical context.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".