Microencapsulated rhEGF to facilitate epithelial healing and prevent scar formation of cesarean wound: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: Cesarean section (CS) is a major surgical intervention that affects women at childbearing age. Scarring from CS potentially causes discomfort and psychological distress. Emerging evidence indicates that epidermal growth factor (EGF) plays crucial roles in wound healing with the potential of minimizing scar formation. This study aims to investigate the effect of microencapsulated recombinant human EGF (Me-EGF) in scar prevention. Silicone gel was incorporated as part of the routine scar treatment. MATERIALS AND METHODS: Healthy women scheduled for cesarean delivery were enrolled and randomized to three groups: (1) no scar treatment, (2) silicone gel only, or (3) silicone gel plus Me-EGF. Vancouver Scar Scale (VSS: vascularity, pigmentation, elasticity, and height) was used for scar assessment at the 6th month and 9th month after CS. RESULTS: A total of 60 women were enrolled, but one patient withdrew due to noncompliance with the follow-up visit requirement. Me-EGF-containing treatment group consistently scored the lowest on every parameter in the VSS scale, followed by silicone gel group, and the group with no scar treatment. Kruskal-Wallis tests indicated significant differences (p < 0.05) between Me-EGF-containing treatment group and the other two groups in vascularity, pigmentation, elasticity, and the VSS total score, at either 6th month, 9th month, or both time points. The only parameter not showing any significant between-group difference was scar height, but the pattern still remained the same, in which Me-EGF group scored better in both month 6 and 9. CONCLUSION: Surgical incisions in lower abdomen posed challenge in scar management. Our findings suggest that Me-EGF is a potential therapeutic option for better wound healing and scar prevention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".