Youngest Composite Full-Face Transplant
Bibliographic record
Abstract
BACKGROUND: The field of face transplantation continues to evolve, with more complex defects being addressed, and, at the same time, increased outcome expectations. Given our unique long-term experience in this field, we consented one of the youngest patients to undergo a full-face transplant. METHODS: An 18-year-old woman presented with complete destruction of her central face and craniofacial structures. She had coexisting major injuries, including pituitary gland, visual axis, and motor control. After extensive rehabilitation and reconstruction techniques, the patient underwent face transplant on May 4, 2017, at the age of 21 years. RESULTS: The total operative time for the recipient was 26 hours. There were no major perioperative complications. Since transplant, the patient has undergone 3 revision surgeries. She is near completely independent from a daily life activity standpoint. She has had 1 episode of rejection above grade II that was successfully treated with a short-term increased in immunosuppression. CONCLUSIONS: Contrary to data in solid organ transplantation where youth is associated with increased risk of rejection, our current algorithm in immunosuppression, combined with this patient's compliance, has led to only 1 rejection episode beyond grade II. This successful transplant can serve as a model for future vascularized composite transplants in younger populations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".