Utility Scores for Facial Disfigurement Requiring Facial Transplantation [Outcomes Article]
Bibliographic record
Abstract
BACKGROUND: Controversy exists as to whether the benefits of facial transplantation outweigh the risk of continuous immunosuppression. Utility scores [range, 0 (death) to 1 (perfect health)] are a standardized tool with which to objectify health states or diseases and can help answer such controversy. METHODS: An Internet-based utility assessment study using visual analogue scale, time trade-off, and standard gamble was used to obtain utilities for facial disfigurement requiring facial transplantation from a sample of the general population and medical students at McGill University. Average utility scores were compared using t test, and linear regression was performed using age, race, and education as independent predictors of each of the utility scores. RESULT: A total of 307 people participated in the study. All measures (visual analogue scale, time trade off, and standard gamble) for facial disfigurement (0.46 + or - 0.02, 0.68 + or - 0.03, and 0.66 + or - 0.03, respectively) were significantly different (p < 0.001) from the corresponding ones for monocular blindness (0.62 + or - 0.02, 0.83 + or - 0.02, and 0.82 + or - 0.02, respectively) and binocular blindness (0.33 + or - 0.02, 0.62 + or - 0.03, and 0.61 + or - 0.03, respectively). Age was inversely proportional to the utility scores in all groups (p < 0.01), decreasing a utility score of 0.006 for every increase in year of age. CONCLUSION: A sample of the general population and medical students, if faced with facial disfigurement, would undergo a face transplant procedure with a 34 percent chance of death and be willing to trade 12 years of their life to attain perfect health.
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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 | 0.002 | 0.007 |
| 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) | 0.007 | 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 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".