Role of a skin bridge incision and prophylactic incisional negative-pressure wound therapy in the prevention of surgical site infection after inguinal lymph node dissection
Bibliographic record
Abstract
Background: Modification of the surgical technique to a 2-incision technique with skin bridge from the traditional lazy S (LS) incision, as well as use of prophylactic incisional negative-pressure wound therapy (iNPWT), are theorized to reduce the risk of surgical site infection (SSI) after inguinal lymph node dissection (ILND). We sought to investigate the role of a perioperative ILND bundle on adverse events after ILND and lymph node harvest. Methods: We performed a retrospective review of patients who underwent ILND before and after implementation of the ILND bundle (September 2016) at 1 centre in southeastern Ontario between 2013 and 2018. The ILND bundle included a skin bridge incision, running subcuticular skin closure and NPWT. Previously, an LS incision was used, with stapled skin closure and conventional dressing. Development of SSI was the primary outcome, and dehiscence and seroma and hematoma formation were secondary outcomes. We estimated the associations using multivariable logistic regression. Results: Thirty-four ILNDs in 33 patients were included, 15 in the LS incision group and 19 in the perioperative bundle group. The baseline demographic characteristics of the 2 groups were similar. The perioperative bundle was associated with a reduction in the SSI rate (11 [73%] v. 6 [32%], p = 0.02) and elimination of wound dehiscence (0 [0%] v. 5 [33%], p = 0.006). On multivariable logistic regression, it was associated with a 5.9-fold reduction in the SSI rate (odds ratio 0.17, 95% confidence interval 0.03–0.74). Conclusion: The results suggest a decrease in SSI rates with use of a perioperative bundle compared to the LS incision and a standard dressing. Randomized controlled trials are required to better understand the associations among the skin bridge incision, iNPWT and SSI.
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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) | 0.000 | 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".