Using a Silicone Gel Prosthesis for Burn Scar Camouflage: A Case Report
Bibliographic record
Abstract
The scarring that occurs following a burn injury can have devastating physical and emotional consequences for the patient. Scar revision surgery can be very effective, but when surgical options are exhausted or the patient declines, camouflage is an alternative. A 43-year-old woman with mid-deep dermal flame burns sustained in a house fire in August 2020, comprising 6% total body surface area, underwent chemical debridement followed by allograft application. One month later, the patient underwent surgical debridement with a split-thickness skin graft to an unhealed area on her left posterior shoulder. This procedure was successful, with 100% graft take. During her follow-up visits, she developed hypertrophic scarring in this region, which shifted the focus of her care to scar management. We developed a custom-made silicone gel prosthesis to camouflage the scar, help the patient regain confidence, and also provide therapeutic benefit by sealing in hydration. The prosthesis was matched to the patient's normal skin tone and texture, featuring a soft and flexible outer surface with a smooth inner surface for adherence. The patient found the prosthesis easy to apply and inconspicuous, yet effective at concealing the scarred area. The prosthesis also offered protection from external sources of irritation. Both the Patient and Observer Scar Assessment Scale and the Modified Vancouver Scar Scale showed marked improvement when the prosthesis was used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".