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Record W2092725101 · doi:10.1177/0272989x03258421

Validity of Standard Gamble Utilities as Measured by Transplant Readiness in Lung Transplant Candidates

2003· article· en· W2092725101 on OpenAlexaff
L.G. Singer, James Theodore, Michael K. Gould

Bibliographic record

VenueMedical Decision Making · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsConcordanceMedicineQuality of life (healthcare)PopulationTransplantationListing (finance)Lung transplantationActuarial scienceIntensive care medicineSurgeryInternal medicineEnvironmental healthFinanceNursing

Abstract

fetched live from OpenAlex

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 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)0.026
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.211
GPT teacher head0.423
Teacher spread0.213 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2003
Admission routes1
Has abstractyes

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