Face Transplant: Current Update and First Canadian Experience
Bibliographic record
Abstract
SUMMARY: Facial vascularized composite allotransplantation has emerged as a groundbreaking reconstructive solution for patients with severely disfiguring facial injuries. The authors report on the first Canadian face transplant. A 64-year-old man sustained a gunshot wound, which resulted in extensive midface bony and soft-tissue damage involving the lower two-thirds of the face. In May of 2018, he underwent a face transplant consisting of Le Fort III and bilateral sagittal split osteotomies in addition to skin from the lower two-thirds of the face and neck. Virtual surgical planning was used to fabricate osteotomy guides and stereolithographic models. Microsurgical anastomoses of the facial (three branches) and infraorbital nerves were performed bilaterally. At 18-month follow-up, the aesthetic outcome was excellent. Partial restoration of light touch sensation had been observed over the majority of the allograft. Although significantly affected, animation, speech, mastication, and deglutition were continuously improving with intensive therapy. Nevertheless, the patient was now tracheostomy and gastrostomy free. Despite these limitations, he reported a high degree of satisfaction with the procedure and had reintegrated into the community. Four grade I episodes of acute rejection with evidence of endotheliitis were successfully treated. Postoperative complications were mainly infectious, including mucormycosis of the left thigh, treated with surgical resection and antifungal therapy. Undoubtedly, immunosuppression represents the greatest obstacle in the field and limits the indications for facial vascularized composite allotransplantation. Continuous long-term follow-up is mandatory for surveillance of immunosuppression-related complications and functional assessment of the graft.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".