Health state utility valuation in radioactive iodine-refractory differentiated thyroid cancer
Bibliographic record
Abstract
Beth A Fordham,1 Cicely Kerr,1 Hayley M de Freitas,1 Andrew J Lloyd,1 Karissa Johnston,2 Corey L Pelletier,3 Gabriel Tremblay,3 Anna Forsythe,3 Bryan McIver,4 Ezra EW Cohen5 1ICON Patient Reported Outcomes, Oxford, UK; 2ICON Epidemiology, Toronto, ON, Canada; 3Eisai Inc., NJ, 4Moffitt Cancer Center, FL, 5University of California San Diego Moores Cancer Center, La Jolla, CA, USA Purpose: The aim of this study was to elicit utilities for radioactive iodine-refractory differentiated thyroid cancer (RR-DTC) and evaluate the impact of treatment response and toxicities on quality of life.Patients and methods: RR-DTC health states were developed based on data from a previous qualitative study and iterative review by clinical experts. Following piloting, health states underwent valuation by 100 members of the UK public during time trade-off interviews. Mean utilities and descriptive distribution statistics were calculated, and a logistic regression analysis was conducted.Results: The demographic characteristics of the study sample were generally reflective of the UK population. Clear differentiation in valuation between health states was observed. No response/stable disease had an adjusted utility value of 0.87, with a corresponding gain of +0.04 following a treatment response and a decline of –0.35 for disease progression. Adverse events were associated with utility decrements between –0.47 (grade III diarrhea) and –0.05 (grade I/II alopecia).Conclusion: The trade-off interviews derived utility weights show clear differentiation between RR-DTC health states in response to treatment. The values reported in this study are suitable for cost-effectiveness evaluations for new treatments in RR-DTC. Keywords: differentiated thyroid cancer, radioactive iodine-refractory, health-related quality of life, health utility, vignette
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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 | 0.029 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".