Incidence of Hematoma Following Breast Reduction in Patients Taking Selective Serotonin Reuptake Inhibitor or Serotonin-Norepinephrine Reuptake Inhibitor: A Retrospective Review
Bibliographic record
Abstract
Background Selective serotonin reuptake inhibitor (SSRI) and serotonin-norepinephrine reuptake inhibitor (SNRI) use is more common in the plastic surgery population compared to the general population. This study was designed to assess the theoretical effect of SSRIs and SNRIs on platelet function and the potential for increased bleeding risk. This study sought to establish the incidence of postoperative bleeding following routine bilateral breast reduction for patients on SSRIs or SNRIs. The outcomes of this study contribute to the discussion of whether these medications should be discontinued before elective surgery. Methodology A retrospective chart review of all patients who received bilateral breast reduction surgery over a 10-year period was performed. Patient charts were reviewed for postoperative hematoma formation as well as medications being used around the time of surgery. The rate of hematoma formation in patients actively taking SSRIs or SNRIs at the time of surgery was compared with the rest of the study population. Results A total of 1,022 patients met the inclusion criteria for the study. The overall incidence of postoperative hematoma was 7.7%. Of these, 1.9% of patients had clinically significant hematomas that required operative evacuation, and the remaining were treated conservatively. The only variable associated with a significantly higher risk of hematoma formation was advanced age (p = 0.005). Conclusions There was no significant difference in hematoma incidence after breast reduction in patients taking SNRIs or SSRIs compared with the general population. This contradicts some of the previously published literature and can hopefully guide clinicians in counseling their patients preoperatively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".