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Record W2050951525 · doi:10.1097/moo.0b013e32832cba57

Facial transplantation in a new era: what are the ethical implications?

2009· review· en· W2050951525 on OpenAlexaff
Stephanie E Johnson, Martin Corsten

Bibliographic record

VenueCurrent Opinion in Otolaryngology & Head & Neck Surgery · 2009
Typereview
Languageen
FieldMedicine
TopicOrgan and Tissue Transplantation Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineTransplantationIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.161
GPT teacher head0.441
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2009
Admission routes1
Has abstractyes

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