Facial transplantation in a new era: what are the ethical implications?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Facial transplantation has become a clinical reality, with the successful completion of five procedures to date in France, China, and most recently the United States. The technical and immunological obstacles associated with this procedure have largely been overcome. There is ongoing debate, however, regarding the ethical implications of the procedure. RECENT FINDINGS: The early literature in facial transplantation stressed the ethical concerns regarding the procedure and recommended that the procedure not be attempted. Recently, however, ethicists and surgeons have created strict ethical guidelines regarding patient selection, informed consent, and accurate evaluation of the risks of the procedure - both medical and psychological. Research has demonstrated that individuals would be willing to accept significant risk to undergo facial transplantation, given the devastating nature of facial disfigurement. In addition, no insurmountable technical or immunological obstacles have been identified to prevent the further success of facial transplantation. SUMMARY: The ethical issues associated with facial transplantation will be further refined as more experience is gained with the procedure. With these strict ethical guidelines and with significant efforts in patient selection and preoperative counseling, facial transplantation will likely continue to remain a viable option for patients with significant facial disfigurement.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".