The efficacy of dermofat grafts from the groin for correction of acquired facial deformities
Bibliographic record
Abstract
BACKGROUND: Posttraumatic acquired facial deformities require surgical treatment, with options including scar revision, fat grafts, implant insertion, and flap coverage. However, each technique has specific advantages and disadvantages. . METHODS: From 2016 to 2018, 13 patients (eight with scar contracture and five with a depressed scar) were treated using dermofat grafts from the groin. The harvested dermofat was then inserted into the undermined dead space after the contracture was released, and a bolster suture was done for fixation considering the patient's contour and asymmetry. A modified version of the Vancouver Scar Scale and satisfaction survey were used to compare deformity improvements before and after surgery. . RESULTS: In most cases, effective volume correction and an aesthetically satisfactory contour were maintained well after dermofat grafting, without any major complications. In some cases, however, lipolysis proceeded rapidly when inflammation and infection were not completely eliminated. A significant difference was found in the modified Vancouver Scar Scale before and after surgery, with a p-value of 0.001. The average score on the satisfaction survey was 17.07 out of 20 points. . CONCLUSION: A dermofat graft with the groin as the donor site can be considered as an effective surgical option that is the simplest and most cost-effective method for the treatment of acquired facial deformities with scar contracture.
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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) | 0.002 | 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".