Validation of China Health-Related Outcomes Measures-Cardiovascular Disease
Bibliographic record
Abstract
OBJECTIVES: China Health-Related Outcomes Measures (CHROME) was an initiative aimed at developing a system of preference-based health-related quality of life instruments for China. CHROME-cardiovascular disease (CVD) is a CVD-specific instrument with 14 items developed under this initiative. This study aimed to test the psychometric properties of CHROME-CVD. METHODS: This validation study was conducted using cross-sectional questionnaire survey in China. Eligible patients with CVD were recruited and asked to complete the CHROME-CVD, the EQ-5D-5L, and a CVD-specific nonpreference-based health-related quality of life instrument selected according to the confirmed diagnosis of the patients. Item evaluation, internal consistency, measurement invariance, test-retest reliability, structural validity, and construct validity were tested using classic test theory. Item response theory was used to evaluate item-level performance. RESULTS: A total of 444 patients with CVD (coronary artery disease, n = 276, heart failure, n = 104, angina, n = 33, and atrial fibrillation, n = 16) from 6 provinces in China were enrolled for the validation. Exploratory factor analysis identified 4 factors: chest pain, other symptoms, physical health, and mental and social health. Cronbach's alpha and intraclass correlation coefficient were >0.8. A total of 20 of 26 (76.9%), and 90 of 95 (94.7%) predefined hypotheses were met for convergent and discriminant validities, respectively. No important difference was identified between subgroups of gender and residency. Response options of 10 items were found overlapped based on categorical response curves, which led to modification to 4-level response options. Wording of 3 items were modified by referring wordings of reference instruments. CONCLUSION: The validation of the CHROME-CVD demonstrated generally good psychometric properties. Further validation on the modified CHROME-CVD is needed.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".