The generic version of China Health Related Outcomes Measures (CHROME-G): psychometric testing and comparative performance with the EQ-5D-5L and SF-6Dv2 among the Chinese general population
Bibliographic record
Abstract
Abstract Objectives The CHROME-G is the first generic preference-based measure developed in China. This study aimed to validate and compare the psychometric properties of the CHROME-G with the EQ-5D-5L and SF-6Dv2 among the Chinese general population. Methods A representative sample of the Chinese general population in terms of age, gender, education, and urban/rural residence was recruited for an online survey. During the survey, respondents completed three instruments (first the CHROME-G, then the EQ-5D-5L and SF-6Dv2 in random order), demographic and health-related questions. The retest survey was carried out after two weeks. Ceiling/floor effects were first assessed. Convergent and divergent validity was examined using Spearman’s rank correlation. Known-group validity was examined using the non-parametric Kruskal–Wallis H test and effect size. Test–retest reliability was assessed using the intraclass correlation coefficient and weighted Kappa statistics. Results One thousand respondents (51.1% male, mean age 44.7 years) completed the first survey, with 378 also completing the retest survey. The mean ± SD completion time was 2.03 ± 0.58 min for the CHROME-G, and 1.37 ± 0.54 and 1.13 ± 0.38 min for the EQ-5D-5L and SF-6Dv2. Only the EQ-5D-5L had a ceiling effect of 35.1%. The range of Spearman rank’s correlations was 0.45–0.62 for convergent validity and 0.14–0.46 for divergent validity. Among different health subgroups, the effect size for the CHROME-G, EQ-5D-5L and SF-6Dv2 was 1.348–3.416, 1.362–3.325 and 1.097–2.228, respectively. The ICC for test–retest was 0.791 for the CHROME-G, compared with 0.994 and 0.971 for the EQ-5D-5L and SF-6Dv2. Conclusions The CHROME-G showed good and comparable psychometric properties with the EQ-5D-5L and SF-6Dv2.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".