(CONSORT) Wound closure using Dermabond after excision of hemangioma on the lip
Bibliographic record
Abstract
BACKGROUND: As the lip contains ample blood supply, hemangiomas often occur in this area. When surgical excision is performed, wound closure is important. To prevent infection from saliva and food, watertight wound closure is needed. The purpose of this study is to demonstrate the usefulness of Dermabond for wound closure after hemangioma excision on the lip. METHODS: Between December 2015 and August 2017, 11 patients with lip hemangioma underwent surgical excision. When closing the wound, Dermabond was used for skin closure after subcutaneous sutures. Demographic data and complications were recorded. Scars were evaluated with the Vancouver scar scale (VSS), and the postoperative shape of the lip was assessed on a 10-point satisfaction scale at 1 month and 6 months postoperatively. RESULTS: All cases completely healed without any complications, such as wound dehiscence or infection. There were no recurrences at postoperative 1 month during the follow-up period. The aesthetic results of the scars were also excellent. The average VSS score on postoperative 1 month was 4.2, and it decreased to 2.2 at postoperative 6 months. The average patient satisfaction score at postoperative 1 month was 7.4, and it increased to 9.5 at postoperative 6 months. CONCLUSION: Dermabond is useful for wound closure after hemangioma excision on the lip. It prevents wound contamination, and yields acceptable aesthetic results.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".