Composite tissue allotransplantation of the face: Decision analysis model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Facial composite tissue allotransplantation is a potential reconstructive option for severe facial disfigurement. The purpose of the present investigation was to use decision analysis modelling to ascertain the expected quality-adjusted life years (QALYs) gained with face transplantation (versus remaining in a disfigured state) in an effort to assist surgeons with the decision of whether to adopt this procedure. STUDY DESIGN: The probabilities of potential complications associated with facial allotransplantation were identified by a comprehensive review of kidney and hand transplant literature. A decision analysis tree illustrating possible health states for face allotransplantation was then constructed. Utilities were obtained from 30 participants, using the standard gamble and time trade-off measures. The utilities were then translated into QALYs, and the expected QALYs gained with transplantation were computed. RESULTS: Severe facial deformity was associated with an average of 7.34 QALYs. Allotransplantation of the face imparted an expected gain in QALYs of between 16.2 and 27.3 years. CONCLUSIONS: The current debate within the medical community surrounding facial composite tissue allotransplantation has centred on the issue of inducing a state of immunocompromise in a physically healthy individual for a non-life-saving procedure. However, the latter must be weighed against the potential social and psychological benefits that transplantation would confer. As demonstrated by a gain of 26.9 QALYs, participants' valuation of quality of life is notably greater for face transplantation with its side effects of immunosuppression than for a state of uncompromised physical health with severe facial disfigurement.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it