Prophylactic Negative Pressure Wound Therapy for Closed Laparotomy Incisions
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether negative pressure wound therapy (NPWT) applied to primarily closed incisions decreases surgical site infections (SSIs) following open abdominal surgery. BACKGROUND: SSIs are a common cause of morbidity following open abdominal surgery. Prophylactic NPWT has shown promise for SSI reduction. However, the results of randomized controlled trials (RCTs) conducted among patients undergoing laparotomy have been inconsistent. METHODS: We performed a meta-analysis of English language RCTs comparing the use of prophylactic NPWT to standard dressings on primarily closed laparotomy incisions following open abdominal surgery. Medline, EMBASE, Cochrane Library, and CINAHL databases were searched from inception to December 31, 2018, for relevant studies. A random-effects model was used for statistical analysis. RESULTS: Five RCTs totaling 792 patients were included in our meta-analysis after application of our exclusion and inclusion criteria. There was no significant difference in the risk of SSIs identified among those patients who had NPWT compared to standard dressings; relative risk (RR) 0.56 (95% confidence interval 0.30-1.03, P = 0.064). There was significant statistical heterogeneity across studies (I = 67.4%; P = 0.015). CONCLUSION: The adoption of NPWT for routine SSI prophylaxis following laparotomy is currently not supported and should be used primarily in the context of a clinical trial.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".