Antibiotic Irrigation of Pocket for Implant-Based Breast Augmentation to Prevent Capsular Contracture: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: In vitro and in vivo studies have described a number of different antibiotic solutions for irrigation of the pocket in implant-based breast augmentation in an attempt to prevent the formation of capsular contracture (CC). Our objective was to evaluate the evidence that antibiotic irrigation reduced the rate of CC. METHODS: A systematic search of MEDLINE, EMBASE, and CENTRAL was conducted from inception to January 2016. We included studies which examined the use of intraoperative antibiotic irrigation in women undergoing primary breast augmentation. Our primary outcome was the rate of CC. Included studies were assessed for methodological quality using validated tools. RESULTS: Seven studies were included in the final analysis: 1 randomized controlled trial (RCT) and 6 non-randomized studies. The mean follow-up ranged from 14 to 72 months. The rate of CC was less than 2% in 8 studies, between 3% and 6% in 4 studies, and 13.9% in 1 study. Included studies demonstrated significant clinical and methodological heterogeneity. The solitary low-quality RCT concluded that antibiotic irrigation was superior to saline irrigation. Three non-randomized studies demonstrated no significant difference in the rate of CC with the use of antibiotics. One non-randomized controlled study showed that the use of mixture of antibiotic and povidone-iodine significantly lowered the rate of CC. CONCLUSIONS: The available evidence on the use of antibiotic irrigation to prevent CC is weak and it is based on studies with high risk of bias. Methodologically robust studies are necessary to answer the question whether antibiotic breast pocket irrigation prevents CC.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".