Closed Incision Negative Pressure Therapy Versus Traditional Dressings for Low Transverse Abdominal Incisions Healing by Primary Closure: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Closed incision negative pressure therapy (ciNPT) devices may reduce wound healing complications when applied to closed surgical incisions. The aim of this review was to assess the effects of ciNPT versus standard dressings in patients undergoing primary closure of high tension, lower transverse abdominal incisions. Methods: This review was registered a priori on PROSPERO (CRD42021252048). A search of the following databases was performed in February 2021: Medline, EMBASE, and CENTRAL. Unpublished trials were searched using clinicaltrials.gov. All randomized and nonrandomized studies comparing ciNPT to standard dressings were included. Two independent reviewers performed screening and data extraction. Outcomes evaluated the incidence of wound dehiscence, surgical site infection, total abdominal complications, time to drain removal, and seroma formation. Main Results: Ten studies were included in quantitative and narrative synthesis. Observational study evidence suggests ciNPT likely reduces the incidence of wound dehiscence (odds ratio [OR] 0.57 [0.44-0.96], P = .03) and total abdominal complications (OR 0.34 [0.21-0.54], P < .01). Decreased incidence of seroma formation favored ciNPT (OR 0.65 [0.24-1.76], P = .40); however, this did not achieve significance. Randomized and non-randomized study evidence was very uncertain about the effect of ciNPT on the remaining outcomes. Conclusions: The current best randomized study evidence is very uncertain about the effect of ciNPT on these outcomes. Observational study evidence suggests ciNPT likely results in a statistically significant reduction in abdominal wound dehiscence and total abdominal complications. Additional randomized trials are warranted to limit the impact of bias on the overall certainty of the evidence.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.004 | 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".