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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".