Meta-analysis, Meta-regression, and GRADE Assessment of Randomized and Nonrandomized Studies of Incisional Negative Pressure Wound Therapy Versus Control Dressings for the Prevention of Postoperative Wound Complications
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the efficacy of iNPWT for the prevention of postoperative wound complications such as SSI. SUMMARY OF BACKGROUND DATA: The 2016 WHO recommendation on the use of iNPWT for the prevention of SSI is based on low-level evidence, and many trials have been published since. Preclinical evidence suggests that iNPWT may also prevent wound dehiscence, skin necrosis, seroma, and hematoma. METHODS: PubMed, EMBASE, CINAHL, and CENTRAL were searched for randomized and nonrandomized studies that compared iNPWT with control dressings. The evidence was assessed using the Cochrane Risk of Bias Tool, the Newcastle-Ottawa scale, and GRADE. Meta-analyses were performed using random-effects models. RESULTS: High level evidence indicated that iNPWT reduced SSI [28 RCTs, n = 4398, relative risk (RR) 0.61, 95% confidence interval [CI]: 0.49-0.76, P < 0.0001, I = 27%] with a number needed to treat of 19. Low level evidence indicated that iNPWT reduced wound dehiscence (16 RCTs, n = 3058, RR 0.78, 95% CI: 0.64-0.94). Very low-level evidence indicated that iNPWT also reduced skin necrosis (RR 0.49, 95% CI: 0.33-0.74), seroma (RR 0.43, 95% CI: 0.32-0.59), and length of stay (pooled mean difference -2.01, 95% CI: -2.99 to 1.14). CONCLUSIONS: High-level evidence indicates that incisional iNPWT reduces the risk of SSI with limited heterogeneity. Low to very low-level evidence indicates that iNPWT also reduces the risk of wound dehiscence, skin necrosis, and seroma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.132 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.038 | 0.094 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".