Quantification of Erythema Associated With Varying Suture Materials in Facial Surgery Repair: A Randomized Prospective Study
Bibliographic record
Abstract
BACKGROUND: A common concern among patients following Mohs micrographic surgery (MMS) is scar appearance and residual erythema. However, few studies have quantitatively compared scar erythema between different suture materials. OBJECTIVE: To quantify erythema intensity (EI) associated with use of percutaneous nylon, irradiated polyglactin-910 (IPG) and fast-absorbing gut (FG) sutures on facial sites. METHODS: After undergoing MMS, 210 patients were randomized to one of 2 groups. Patients in the first group (n = 105) had their defects repaired half with continuous IPG sutures and the other half with nylon sutures; the second group (n = 105) received IPG and FG sutures. Standardized photographs of scars were taken at 1 week, 2 months, and 6 months postoperatively and computer-assisted image analysis was used to quantify EI. RESULTS: The average EI was comparable between all 3 suture materials at 1 week, 2 months, and 6 months. From 1 week to 2 months, EI in nylon, IPG, and FG sutures decreased by 24.8%, 12.8%, and 17.9% (p < .05), respectively. There was no statistically significant difference in EI among suture types between 2 and 6 months. CONCLUSION: Erythema decreased significantly during early scar maturation in all groups and was comparable between all suture materials at 1 week, 2 months, and 6 months.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".