Implausible States: Prevalence of EQ-5D-5L States in the General Population and Its Effect on Health State Valuation
Bibliographic record
Abstract
The EQ-5D is made up of health state dimensions and levels, in which some combinations seem less “plausible” than others. If “implausible” states are used in health state valuation exercises, then respondents may have difficulty imagining them, causing measurement error. There is currently no standard solution: some valuation studies exclude such states, whereas others leave them in. This study aims to address 2 gaps in the literature: 1) to propose an evidence-based set of the least prevalent two-way combinations of EQ-5D-5L dimension levels and 2) to quantify the impact of removing perceived implausible states from valuation designs. For the first aim, we use data from 2 waves of the English General Practitioner Patient Survey ( n = 1,639,453). For the second aim, we remodel a secondary data set of a Discrete Choice Experiment (DCE) with duration that valued EQ-5D-5L and compare across models that drop observations involving different health states: 1) implausible states as defined in the literature, 2) the least prevalent states identified in stage 1, and 3) randomly select states, alongside 4) a model that does not drop any observations. The results indicate that two-way combinations previously thought to be implausible actually exist among the general population; there are other combinations that are rarer, and removing implausible states from an experimental design of a DCE with duration leads to value sets with potentially different characteristics depending on the criterion of implausible states. We advise against the routine removal of implausible states from health state valuation studies.
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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 | 0.136 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".