Meta-analysis of antibiotic prophylaxis in breast reduction surgery
Bibliographic record
Abstract
BACKGROUND: Breast reduction surgery is a very common procedure; however, there is still no consensus as to whether antibiotics should be used perioperatively. OBJECTIVE: To review the world literature and perform a meta-analysis of studies comparing wound infection rates with antibiotic use in breast reduction surgery. METHODS: A literature search was performed using the MEDLINE, Cochrane Database of Systematic Reviews, Cochrane Database of Clinical Trials, Embase and CINAHL databases. Subject headings and relevant subheadings for "Breast", "Breast Reduction", "Reduction Mammaplasty", "Mammaplasty" were combined with "Antibiotics" and "Antibacterial Agents". The list of titles was assessed by the study's authors and abstracts were reviewed. All relevant articles were then independently reviewed by the two primary authors, and Jadad scoring was used to assess the quality of the included articles. RESULTS: From the original search, three randomized controlled trials were included in the meta-analysis of preoperative antibiotics. The meta-analysis revealed a 75% reduction in wound infections with preoperative antibiotics (OR 0.25 [95% CI 0.09 to 0.72]). Because only one randomized controlled trial analyzed postoperative antibiotics, no meta-analysis could be performed. CONCLUSIONS: Preoperative antibiotics should routinely be used before breast reduction surgery. The use of postoperative antibiotics remains controversial. Additional randomized studies investigating postoperative antibiotics are needed.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.061 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".