Face Allotransplantation and Burns
Bibliographic record
Abstract
Burns may represent one of the main indications for face allotransplantation. Severely disfigured faces featuring a devastating appearance and great functional impairments are not only seen as burn sequelae but also occur as a result of other traumatic injuries, oncological surgical resections, benign tumors (eg, neurofibromatosis), and major congenital malformations. To date, 20 human face composite tissue allotransplants have been performed with success. Despite the initial scepticism about its applicability, due mainly to ethical and technical reasons, the previous worldwide cases and their associated positive outcomes, including acceptable immunosuppressive regimens, excellent aesthetic and functional results, and good psychological acceptance by the recipient, enable the conclusion that face composite tissue allotransplantation has become another therapeutic strategy in the reconstructive surgical armamentarium, which bears special consideration when dealing with severely disfigured burned patients. The aim of this review is to describe the basics of face composite tissue allotransplantation and give an overview of some of the cases performed until now, with special attention paid to debating the pros and cons of its applicability in burn patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".