Antibiotic Prophylaxis for Preventing Surgical-Site Infection in Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: There is a growing concern for microbial resistance as a result of overuse of antibiotics. Although guidelines have focused on the use of antibiotics for surgery in general, few have addressed plastic surgery specifically. The objective of this expert consensus conference was to evaluate the evidence for efficacy and safety of antibiotic prophylaxis in plastic surgical procedures. METHODS: THE AUTHORS: searched for existing high-quality systematic reviews for antibiotic prophylaxis in the literature from the MEDLINE, Cochrane Library, and Embase databases. All synonyms for antibiotics were combined with terms for relevant plastic surgery procedures. The searches were not limited by language, and included all study designs. In addition, supplemental hand searches were performed of bibliographies of relevant articles, and extensive "related articles." Meta-analyses were performed and reviewed by experts selected by the American Association of Plastic Surgeons to reach consensus recommendations. RESULTS: Database searches identified 4300 articles, from which 2042 full-text articles were identified for eligibility. De novo meta-analyses were performed for each plastic surgical category. In total, 67 studies met the inclusion criteria, including nine for breast surgery, 17 for head and neck surgery, 10 for orthognathic surgery, seven for rhinoplasty/septoplasty, 19 for hand surgery, five for skin surgery, and two for abdominoplasty. CONCLUSIONS: Systemic antibiotic prophylaxis is recommended for clean breast surgery and for contaminated surgery of the hand or the head and neck. It is not recommended to reduce infection in clean surgical cases of the hand, skin, head and neck, or abdominoplasty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".