Minimally important difference and predictors of change in quality of life in type 2 diabetes: A community‐based survey in China
Bibliographic record
Abstract
BACKGROUND: To identify the minimally important difference (MID) of the EQ-5D-3L and determinants of change in quality of life (QoL) as measured by the EQ-5D-3L over 1 year for Chinese type 2 diabetic patients (T2DPs). METHODS: Clinically diagnosed T2DPs were recruited from 66 community health centres in five Chinese cities using a multistage quota sampling method between December 2010 and October 2011. Demographics, diabetes-related information, and health-related behaviours were collected at baseline. The EQ-5D-3L was administered at baseline and at 12 months. Anchor-based and distribution-based approaches were employed to estimate MIDs. Using the MIDs as cut-points, we identified the change in EQ-5D-3L-measured QoL into "worsening," "no change," and "bettering." Logistic and ordered logistic regressions were conducted for those who reported best possible EQ-5D health state ("best possible HS") and impaired EQ-5D health states ("impaired HS") at baseline, respectively. Explanatory variables included demographics, diabetes-related information, and health-related behaviours. RESULTS: A total of 1958 patients (54.9% female, mean age 61.2 years, mean diabetes duration 7.9 years) were included in our analysis. MIDs of the EQ-5D-3L for deterioration and improvement were estimated as -0.066 to -0.003, and 0.049 to 0.077, respectively. For the impaired HS group, older age, lower education, and less exercise were significant predictors for worsening in QoL; whereas, those predictors were older age, female gender, and lower income for the best possible HS group. CONCLUSIONS: Minimally important differences for deterioration and improvement were estimated for the EQ-5D-3L. Age, gender, education, income, and exercise were significant determinants of QoL change for Chinese T2DPs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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, 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".