Quantification of Erythema Associated With Continuous Versus Interrupted Nylon Sutures in Facial Surgery Repair: A Randomized Prospective Study
Bibliographic record
Abstract
BACKGROUND: Patients are often concerned about the cosmetic appearance of scars following Mohs micrographic surgery (MMS), including residual erythema. However, few studies have compared the cosmetic outcomes between different suturing techniques. OBJECTIVE: To compare the erythema intensity (EI) associated with interrupted sutures (IS) and continuous sutures (CS), and the degree of its reduction over time. MATERIALS AND METHODS: Mohs micrographic surgery patients were randomized to have half of their defect repaired with IS and the other half with CS. Postoperatively, subjects were assessed at 1 week, 2 months, and 6 months and close-up photographs of their scars were taken. Computer-assisted image analysis was utilized to quantify the EI in each half-scar. RESULTS: The average EI of IS was greater than that of CS by 9.3% at 1 week (p < .001) and 7.2% at 2 months (p < .021) but comparable at 6 months. These differences were clinically detectable, but EI differences resolved by 6 months in most cases. At 6 months, EI regressed by 33.5% in IS and 26.3% in CS. CONCLUSION: Continuous sutures are associated with less erythema during early scar maturation but are comparable to IS at 6 months. These results may guide the choice of suturing technique to improve early cosmetic outcomes and overall patient satisfaction.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".