Validity of Standard Gamble Utilities as Measured by Transplant Readiness in Lung Transplant Candidates
Bibliographic record
Abstract
PURPOSE: To evaluate the validity of standard gamble (SG) utilities, by comparing utilities with decision-making behavior in a group of lung transplant candidates facing a risky health decision. METHODS: The authors elicited SG utilities for current health from 57 transplant candidates. They assessed the concordance between utility scores and patients' self-reported readiness to be placed on the transplant waiting list ("listed"). Because transplantation represents a real-life gamble with a short-term survival probability of 85%, the authors defined their minimum validity criterion as utility for current health < or = 0.85 in transplant-ready patients. RESULTS: Utilities were significantly higher in patients who were not ready for listing (n = 22, median utility = 0.79, range 0.06-1) than in those who were ready or listed (n = 35, median utility = 0.50, range 0-0.85, P < 0.00005). All transplant-ready patients had utilities < or = 0.85 for current health. CONCLUSIONS: Low SG utilities were associated with transplant readiness in this population of lung transplant candidates. These results provide one line of evidence supporting the validity of SG utilities as a measure of health-related quality of life, using the criterion of decision-making behavior.
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 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 | 0.020 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".