A Randomized Multicenter Trial to Evaluate Simple Utility Elicitation Techniques in Patients With Gastroesophageal Reflux Disease
Bibliographic record
Abstract
BACKGROUND: Despite recommendations that patients rating their own health using utility and preference measures such as the feeling thermometer (FT) and standard gamble (SG) should also rate hypothetical marker states, little evidence supports marker state use. We evaluated whether the administration of marker states improves measurement properties of the FT and SG. METHODS: We randomized 217 patients with gastroesophageal reflux disease to complete the FT (self-administered) and SG with marker states (FT+ / SG+, n = 112) or without marker states (FT- / SG-, n = 105) before and after 4 weeks of treatment with a proton pump inhibitor, esomeprazole. Patients also completed other health-related quality of life instruments. RESULTS: The use of marker states did not influence baseline utility scores (FT+ 0.66, FT- 0.68; SG+ 0.77, SG- 0.78, on a scale from 0 [dead] to 1.0 [full health]). Improvement after therapy was 0.21 in FT+ and 0.15 in FT- (both P < 0.001; difference between FT+ and FT- = 0.06, P = 0.02). Improvement in SG+ was 0.07 (P < 0.001) and 0.06 in SG- (P = 0.003) (difference between SG+ and SG- = 0.01, P = 0.63). Correlations with other health-related quality of life scores were generally stronger, with some statistically significant differences in correlations, for FT+ compared with FT-, but tended to be weaker for SG+ compared with SG-. CONCLUSION: The administration of marker states improved the responsiveness and validity of the FT but not of the SG. Decisions about administering marker states should depend on whether the FT and SG is of primary interest and the importance of optimal validity and responsiveness relative to competing objectives such as efficiency.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".