Improving the Appearance of Surgical Facial Scars With IncobotulinumtoxinA and Microneedling
Bibliographic record
Abstract
Background: The appearance of post-surgical scars on the face is a major concern for surgeons and a source of anxiety for patients after Mohs surgery due to nonmelanoma skin cancer (NMSC). The objective of this retrospective study was to assess the effectiveness of combining incobotulinumtoxinA and microneedling to improve the appearance of post-operative facial scars. Enrolled subjects underwent surgical removal of facial NMSCs followed by flap reconstruction by the same surgeon during 2014 (n=35) and 2015 (n=35). Sutures were removed 7 days after the procedure. Subjects treated during 2014 received no additional treatment and served as a control group. Subjects treated during 2015 also received micro-doses of incobotulinumtoxinA along the scar border and microneedling of the surgical area. Microneedling was repeated after 15 days. Scar severity was determined by the surgeon and an independent dermatologist using the modified Vancouver Scar Scale (VSS) scores on day 7 and day 30 following suture removal. Patient Satisfaction Scale scores were also determined using a 5-point scale on day 30. Mean (SD) VSS scores were 10.4 (1.14) on day 7 among treated subjects vs. 9.5 (1.88) among control subjects (P<0.05). On day 30, mean VSS scores had decreased to 1.1 (0.89) for treated subjects vs. 7.6 (1.72) for control subjects (P<0.05). Patient Satisfaction Scores were significantly higher among treated patients vs control subjects (4.45 vs 3.14; P<0.001). The use of incobotulinumtoxinA is a promising therapeutic option for improving scar appearance. Combined with microneedling, it significantly reduced VSS scores and improved overall satisfaction of treated subjects following surgery for NMSCs. J Drugs Dermatol. 2020;19(6): doi:10.36849/JDD.2020.4772
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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 | 0.000 |
| 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".