Health-related quality of life deficits associated with varying degrees of disease severity in type 2 diabetes.
Bibliographic record
Abstract
BACKGROUND: Diabetes is a chronic medical condition accompanied by a considerable health-related quality of life (HRQL) burden. The purpose of this analysis was to use generic measures of HRQL to describe HRQL deficits associated with varying degrees of severity of type 2 diabetes. METHODS: The RAND-12 physical and mental health composites (PHC and MHC, respectively) and Health Utilities Index Mark 3 (HUI3) were self-completed by 372 subjects enrolled in a prospective, controlled study of an intervention to improve care for individuals with type 2 diabetes in rural communities. Analysis of covariance was used to assess differences in HRQL according to disease severity and control of blood glucose. Disease severity was defined in terms of treatment intensity, emergency room visits and absenteeism from work specifically attributable to diabetes. To control for potential confounding, the analysis was adjusted for important sociodemographic and clinical characteristics. RESULTS: The PHC and MHC were significantly lower for individuals treated with insulin as compared to diet alone (PHC: 41.01 vs 45.11, MHC: 43.23 vs 47.00, p < 0.05). Individuals treated with insulin had lower scores on the vision, emotion and pain attributes of the HUI3 than individuals managed with oral medication or diet. The PHC, MHC, pain attribute and overall score on the HUI3 captured substantial decrements in HRQL associated with absenteeism from work due to diabetes, while the burden associated with emergency room utilization for diabetes was seen in the PHC and HUI3 pain attribute. CONCLUSIONS: We concluded that generic measures of HRQL captured deficits associated with more severe disease in type 2 diabetes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".