Darbepoetin Alfa Impact on Health Status in Diabetes Patients with Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Quality of life (QOL) is markedly impaired in patients with anemia, diabetes mellitus, and chronic kidney disease. Limited data exist regarding the effect of anemia treatment on patient perceptions. The objectives were to determine the longitudinal impact of anemia treatment on quality of life in patients with diabetes and chronic kidney disease and to determine the predictors of baseline and change in QOL. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In a large, double blind study, patients with type 2 diabetes mellitus, nondialysis chronic kidney disease (estimated GFR, 20 to 60 ml/min per 1.73 m(2)), and anemia (hemoglobin 10.4 g/dl) were randomized to darbepoetin alfa or placebo. QOL was measured with Functional Assessment of Cancer Therapy-Fatigue, Short Form-36, and EuroQol scores over 97 weeks. RESULTS: Patients randomized to darbepoetin alfa reported significant improvements compared with placebo patients in Functional Assessment of Cancer Therapy-Fatigue, and EuroQol scores visual analog scores, persisting through 97 weeks. No consistent differences in Short Form-36 were noted. Consistent predictors of worse change scores include lower activity level, older age, pulmonary disease, and duration of diabetes. Interim stroke had a substantial negative impact on fatigue and physical function. CONCLUSION: Darbepoetin alfa confers a consistent, but small, improvement in fatigue and overall quality of life but not in other domains. These modest QOL benefits must be considered in the context of neutral overall effect and increased risk of stroke in a small proportion of patients. Patient's QOL and potential treatment risk should be considered in any treatment decision.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".