Validation of the Generic Version of China Health Related Outcomes Measures (CHROME‐G) among individuals with type 2 diabetes in China
Bibliographic record
Abstract
OBJECTIVE: The Generic Version of China Health Related Outcomes Measures (CHROME-G) was a new preference-based health-related quality of life (HRQoL) instrument designed specifically for the Chinese population. This study aimed to validate and compare measurement properties of CHROME-G with EuroQol-5 Dimensions-5 Levels (EQ-5D-5L), Short Form-6 Dimensions version 2 (SF-6Dv2), and Diabetes-Specific Quality of Life (DSQL) scales among the elderly Chinese population with type 2 diabetes. METHODS: A representative sample population was recruited across the country. Internal consistency was assessed using Cronbach's alpha. Hypotheses testing including convergent validity and known-groups validity were evaluated using Spearman's rank correlation and effect sizes, respectively. Sensitivity was examined using relative efficiency and receiver operating characteristic curve. RESULTS: A total of 131 individuals with type 2 diabetes (54.20% male; mean age 69.03 years) were enrolled. Cronbach's alpha was 0.94 for DSQL, 0.93 for CHROME-G, 0.87 for EQ-5D-5L, and 0.88 for SF-6Dv2. For the convergent validity of CHROME-G, 24/29 (82.76%) correlations met the predefined hypotheses, with Spearman's rank correlation coefficients ranging from 0.51 to 0.96. Among the different health subgroups, the effect sizes for CHROME-G, DSQL, EQ-5D-5L, and SF-6Dv2 were 0.19-1.26, 0.36-1.62, 0.22-1.06, and 0.49-0.87, respectively. CHROME-G, DSQL, and SF-6Dv2 had higher efficiency compared with EQ-5D-5L in detecting differences in self-reported health status, with relative efficiency of 3.18 and 1.76, 4.38 and 6.52, and 1.56 and 2.09, respectively. CONCLUSIONS: CHROME-G demonstrates relatively good measurement properties compared with EQ-5D-5L and SF-6Dv2 for measuring the HRQoL among the elderly Chinese population with type 2 diabetes. The sensitivity of DSQL appears to be better than that of the three generic instruments.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".