The Use of Negative Pressure Wound Therapy for Breast Surgeries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Negative pressure wound therapy (NPWT) following breast surgery has emerged as a promising intervention theorized to reduce complication rates, improve patient-important outcomes, and enhance cost-effectiveness. This systematic review and meta-analysis aims to determine outcomes of NPWT following breast surgery. Methods: MEDLINE, Embase, CINAHL, Web of Science, and CENTRAL were searched to include all English-language, peer-reviewed observational and randomized controlled trials (RCTs) investigating NPWT on the breast or donor site among patients undergoing breast surgery. Studies evaluated at least one of the following outcomes: wound dehiscence, surgical site infection (SSI), implant loss, re-operation, re-admission, hematoma, seroma, and skin/wound necrosis. Quality of evidence was assessed with GRADE methodology. Results: This review includes 31 studies (eight RCTs, 23 observational) analyzing 3320 patients (4326 breasts). High certainty of evidence indicates decreased risk of wound dehiscence among NPWT patients in RCTs for all NPWT application sites (donor: 0.40; 95%CI 0.21, 0.79; breast: 0.59; 95%CI 0.41, 0.84) and observational trials where NPWT was placed on donor sites (0.64; 95%CI 0.42, 0.98). Some evidence indicates NPWT may reduce SSI, hematoma, seroma, and skin/wound necrosis incidence, however results are uncertain and varied in statistical significance. No effect was identified on rates of breast implant loss, re-operation, and re-admission, although this certainty of evidence is very low. Conclusions: Our findings suggest NPWT following breast surgery reduces the risk of wound dehiscence, may have some effect on SSIs, hematoma, seroma, and skin/wound necrosis; and does not demonstrate an effect on rates of implant loss, re-operation or re-admission.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".