Antibiotic Prophylaxis in Plastic Surgery Correlation Between Practice and Evidence
Bibliographic record
Abstract
BACKGROUND: The use of appropriate preoperative antibiotic prophylaxis decreases the risk of surgical site infections (SSI); however, the breadth of plastic surgery procedures makes it challenging to ensure appropriate use for each unique procedure type. Currently, plastic surgeons lack a cohesive and comprehensive set of evidence-based guidelines (EBG) for surgical prophylaxis. We sought to profile the perioperative antibiotic prescribing patterns for plastic surgeons in British Columbia to investigate if they are congruent with published recommendations. In doing so, we aim to determine risk factors for antibiotic overprescribing in the context of surgical prophylaxis. METHODS: A literature review identifying EBG for antibiotic prophylaxis use during common plastic surgery procedures was performed. Concurrently, a provincial survey of plastic surgery residents, fellows, academic and community plastic surgeons was used to identify their antibiotic prophylaxis prescribing practices. These findings were then compared to recommendations identified from our review. The compliance of the provincial plastic surgery community with current EBG was determined for 38 surgical scenarios to identify which clinical factors and procedure types were associated with unsupported antibiotic use. RESULTS: Within the literature, 31 of the 38 categories of surveyed plastic surgery operations have EBG for use of prophylactic antibiotics. When surgical procedures have EBG, 19.5% of plastic surgery trainees and 21.9% of practicing plastic surgeons followed recommended prophylaxis use. Average adherence to EBG was 59.1% for hand procedures, 24.1% for breast procedures, and 23.9% for craniofacial procedures. Breast reconstruction procedures and contaminated craniofacial procedures were associated with a significant reduction in adherence to EBG resulting in excessive antibiotic use. CONCLUSION: Even when evidence-based recommendations for antibiotic prophylaxis exist, plastic surgeons demonstrate variable compliance based on their reported prescribing practices. Surgical procedures with low EBG compliance may reflect risk avoidant behaviors in practicing surgeons and highlight the importance of improving education on the benefits of antibiotic prophylaxis in these clinical situations.
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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 | 0.028 | 0.286 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".