Current Practices and Evidence of Aspirin Usage in Microvascular Surgery: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Acetylsalicylic acid (ASA) has been used in reconstructive microsurgery since the inception of the field. However, when compared to placebo groups, its efficacy is not confirmed. In our study, we hypothesize that the utility of ASA postoperatively in microvascular surgery is not associated with improved outcomes. METHODS: A systematic review of the literature was conducted using PubMed, Google Scholar, and SCOPUS according to PRISMA guidelines. Documentation of antiplatelet regimens and postoperative complications were the primary endpoints. RESULTS: Four articles met inclusion criteria including a total of 1196 patients. There were 637 patients who received aspirin and 559 patients who did not. The average age was not found to be significantly different between the two groups (p > 0.05). In terms of flap type, patients undergoing DIEP had a significantly higher likelihood of receiving aspirin, whereas patients undergoing fibula flaps had a lower rate of aspirin usage (p < 0.05). TRAM, anterolateral thigh flaps, SIEA, and radial forearm flaps were equally distributed between the two groups (p > 0.05). A total of 317 complications were noted across both groups. Total complication rate, complete flap loss, and venous/arterial thrombosis rate were not found to be significantly different between the two groups (p > 0.05). Hematoma rate was found to be significantly higher in the group receiving aspirin when compared to the control (RR = 1.70, 95% CI 1.19-2.44). CONCLUSION: Aspirin usage did not confer significant advantage in preventing postoperative complication rates and increased rates of hematoma formation.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".