RETRACTED: The effect of incisional negative pressure wound therapy on the improvement of postoperative cosmetic suture wounds and scar hyperplasia
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
This study aimed to explore the effects of early incisional negative pressure wound therapy (INPWT) on cosmetic suture wounds and postoperative scar hyperplasia. We retrospectively evaluated 120 patients who underwent abdominoperineal resection at the Changhai Hospital between February 2018 and October 2021 and divided them into two groups according to their treatment: the INPWT group (n = 60) and the control group (n = 60). The quality of post-surgical wound healing in the two groups was evaluated. The Patient Scar Assessment Scale (PSAS), the Vancouver Scar Scale (VSS), and the visual analogue scale (VAS) were used to evaluate the surgical incision scar at 1-year follow-up. At this follow-up visit, 115 patients underwent reexamination; five patients were lost to follow-up, including two patients in the INPWT group and three patients in the control group. The INPWT group showed better wound healing than the control group (P < .05). The proportion of patients who received INPWT was significantly higher in the non-surgical site infection (SSI) group than in the SSI group (P < .05). The PSAS, VSS and VAS scores were significantly improved in the INPWT group compared with those in the control group (P < .05). Our results show that INPWT improved the quality of cosmetic suture wounds and reduced the degree of postoperative scar hyperplasia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".