Effectiveness of Silk Fibroin Dressing Compared to Saline Dressing on Wound Healing in Post-Laparotomy Patients with Superficial Surgical Site Infections: <i>A single-blinded randomised control trial</i>
Bibliographic record
Abstract
Objectives: This study aimed to determine and compare the effectiveness of silk fibroin dressing and normal saline dressing in terms of wound healing time, as assessed by Dr Kamal’s Adaptive Wound Healing Score (KAWHS). Methods: A single-blinded, randomised controlled trial was conducted at a tertiary hospital from November 2022 to November 2023. A total of 142 patients with superficial surgical site infections (SSIs) post-laparotomy were randomised into two groups: silk fibroin dressing (n = 71) and saline dressing (n = 71). The primary outcome, wound healing, was assessed using KAWHS on days 5, 10, and 15. Secondary outcomes included exudate amount (measured by gauze weight), length of hospital stay, and scar characteristics assessed at three months using the Vancouver Scar Scale (VSS). Results: The silk fibroin group demonstrated significantly faster wound healing, with a mean duration of 13.92 ± 2.38 days (95% confidence interval [CI]: 13.36–14.48) compared to 17.41 ± 2.73 days (95% CI: 16.76–18.06) in the saline dressing group (P <0.01). Median KAWHS scores on day 10 were 7 (interquartile range [IQR]: 5–8) versus 9 (IQR: 8–10), and on day 15 were 5 (IQR: 5–5) versus 7 (IQR: 5–8), both favouring silk fibroin (P <0.01). A significant reduction in wound exudate was also observed in the silk fibroin group on days 10 and 15 (P <0.01). No statistically significant differences were found in hospital stay duration (P = 0.32) or scar characteristics at three-month follow-up (P = 0.46). Conclusion: Silk fibroin dressing was more effective than saline dressing in accelerating wound healing and reducing exudate in patients with superficial SSIs following laparotomy. Short-term benefits were evident, whereas long-term outcomes such as scar formation appeared comparable.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".