The Effect of Erythropoietin-Stimulating Agents on Health-Related Quality of Life in Anemia of Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: The efficacy of erythropoietin-stimulating agents (ESAs) for improving health-related quality of life (HRQOL) in anemia of chronic kidney disease (CKD) is unclear. PURPOSE: To determine the effect of ESAs on HRQOL at different hemoglobin targets in adults with CKD who were receiving or not receiving dialysis. DATA SOURCES: Searches of PubMed, EMBASE, the Cochrane Library, and ClinicalTrials.gov from inception to 1 November 2015, supplemented with manual screening. STUDY SELECTION: Randomized, controlled trials that evaluated the treatment of anemia with ESAs, including erythropoietin and darbepoetin, targeted higher versus lower hemoglobin levels, and used validated HRQOL metrics. DATA EXTRACTION: Study characteristics, quality, and data were assessed independently by 2 reviewers. Outcome measures were scores on the Short Form-36 Health Survey (SF-36), Kidney Dialysis Questionnaire (KDQ), and other tools. DATA SYNTHESIS: Of 17 eligible studies, 13 reported SF-36 outcomes and 4 reported KDQ outcomes. Study populations consisted of patients not undergoing dialysis (n = 12), those undergoing dialysis (n = 4), or a mixed sample (n = 1). Only 4 studies had low risk of bias. Pooled analyses showed that higher hemoglobin targets resulted in no statistically or clinically significant differences in SF-36 or KDQ domains. Differences in HRQOL were further attenuated in studies at low risk of bias and in subgroups of dialysis recipients. LIMITATION: Statistically significant heterogeneity among studies, few good-quality studies, and possible publication bias. CONCLUSION: ESA treatment of anemia to obtain higher hemoglobin targets does not result in important differences in HRQOL in patients with CKD. PRIMARY FUNDING SOURCE: KRESCENT and Manitoba Health Research Council Establishment.
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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 | 0.029 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".