Antibiotic Prophylaxis in Alloplastic Breast Reconstruction: Regimens and Outcomes
Bibliographic record
Abstract
Purpose: Surgical site infections (SSI) in prosthesis-based breast reconstruction can have a significant impact on patient outcome. Despite current CDC (Centers for Disease Control and Prevention) guidelines recommending 24 hours of postoperative antibiotics, various perioperative antimicrobial regimens are reported in the literature. Consensus on the optimal duration of antibiotics remains unclear. In this study, the aim is to compare the incidence of surgical site infections following different antibiotic durations in alloplastic breast reconstruction. Methods: In this retrospective cohort study, all consecutive patients who underwent expander/implant-based breast reconstruction between January 2009 and December 2014 at a tertiary centre were included. Data on patient demographics, risk factors, operative time, choice and timing of antibiotic used before surgery, and the duration of postoperative antibiotic use were collected. The primary outcome, SSI, is defined according to CDC criteria. Results: A total of 507 consecutive expander/implant-based cases were included. Minimum follow-up time was 1 year. The overall infection incidence was 14% (95% CI: 11%-17%), and the rate of subsequent explantation was 8%. Of the infected cases, 80% (45/56) received 1 week of postoperative antibiotic, while 20% (11/56) had a prolonged course of antibiotics (2-3 weeks; P = .003, odds ratio [OR] = 2.9; 95% CI: 1.4-5.8). Most infections were superficial (65%). Prior history of radiation treatment was identified as a risk factor for developing surgical site infection ( P = .02). Conclusion: Overall infection rate and risk factors for infections are in keeping with current literature. Prescribing one week of postoperative antibiotic was found to be associated with a higher incidence of SSI compared to a more prolonged antibiotic regimen.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".