Are health states ‘timeless’? A case study of an acute condition: post‐chemotherapy nausea and vomiting<b><sup>1</sup></b>
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The objective was to test whether individuals' responses to standard gamble (SG) and visual analogue scale (VAS) questions do not depend on the time horizon of the health scenario presented. METHODS: Face-to-face interviews were conducted in a convenience sample of 18 women aged 22-50 years with no history of breast cancer or cancer requiring chemotherapy. Data were collected from March 2000 to June 2000 at a university in the Midwest of the United States of America. Preference weights were estimated using SG top-down titration method and VAS scaled from zero (death) to one (perfect health). Subjects were asked to rate their preferences if faced with two scenarios: post-chemotherapy nausea and vomiting (PCNV) occurring for 3 days (scenario 1), and PCNV lasting for the rest of their lives (scenario 2). Three PCNV health states of varying severity were tested: complete alleviation, partial alleviation, and no alleviation. RESULTS: Paired-t-test analysis showed statistically significantly lower preference weights (P < 0.05) when the health state was for the rest of the respondent's life vs. 3 days. Mean SG weights for scenario 1 vs. scenario 2 were: 0.968 vs. 0.927 (complete alleviation), 0.942 vs. 0.810 (partial alleviation) and 0.866 vs. 0.644 (no alleviation). Mean VAS weights for scenario 1 vs. scenario 2 were: 0.741 vs. 0.676 (complete alleviation), 0.490 vs. 0.307 (partial alleviation) and 0.276 vs. 0.136 (no alleviation). DISCUSSION AND CONCLUSIONS: For the majority of respondents the utility independence assumption for SG and VAS did not hold. Similar to Bala et al., the results of this study indicated that preference weights as measured by SG and VAS techniques were not 'timeless'. Regardless of the preference measure used, both SG and VAS yielded higher scores when PCNV lasted for a shorter period of time.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".