Oncologic feasibility for negative pressure wound therapy application in surgical wounds: A meta‐analysis
Bibliographic record
Abstract
Abstract Negative pressure wound therapy (NPWT) decreases postoperative complications of various surgeries. However, the use of NPWT for oncological surgical wounds remains controversial. To evaluate the association of NPWT with oncologic recurrence in surgical wounds without residual malignancy, we analysed studies that compared NPWT with conventional non‐pressure dressings for cancer surgical wounds without residual tumour by August 12, 2020. We compared tumour recurrence rates and postoperative complications between the two procedures. The six studies included 118 patients who received NPWT, and 149 patients who received conventional non‐pressure wound care. The overall quality of the included studies was high based on the Newcastle–Ottawa scale score of 7.5. Tumour recurrence after NPWT was not significantly different compared with conventional non‐negative pressure wound care (9.3% versus 11.4%, P = 0.40). There was no significant heterogeneity between the studies (I2 = 3%). Although NTWT was associated with a lower complication rate compared with the control group, the result was non‐significant (P = 0.15). Application of NPWT in oncologic resection wounds without residual malignancy revealed no difference in local recurrence and may reduce the risk of postoperative complications compared with conventional non‐negative pressure dressings. NPWT can be considered an alternative method for reconstruction in challenging cases.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".