Incisional negative pressure therapy reduces complications and costs in pressure ulcer reconstruction
Bibliographic record
Abstract
Complications after pressure ulcer reconstruction are common. A complication rate of 21% to 58% and a 27% wound recurrence has been reported. The aim of this study was to decrease postoperative wound-healing complications with incisional negative pressure wound therapy (iNPWT) postoperatively. This was a prospective non-randomised trial with a historic control. Surgically treated pressure ulcer patients receiving iNPWT were included in the prospective part of the study (Treatment group) and compared with the historic patient cohort of all consecutive surgically treated pressure ulcer patients during a 2-year period preceding the initiation of iNPWT (Control). There were 24 patients in the Control and 37 in the Treatment groups. The demographics between groups were similar. There was a 74% reduction in in-hospital complications in the Treatment group (10.8% vs 41.7%, P = 0.0051), 27% reduction in the length of stay (24.8 vs 33.8 days, P = 0.0103), and a 78% reduction in the number of open wounds at 3 months (5.4 vs 25%, P = -0.0481). Recurrent wounds and history of previous surgery were risk factors for complications. Incisional negative pressure wound therapy shortens hospital stay, number of postoperative complications, and the number of recurrent open wounds at 3 months after reconstructive pressure ulcer surgery, resulting in significant cost savings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".