The Impact of Selecting a High Hemoglobin Target Level on Health-Related Quality of Life for Patients With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Treatment of anemia in chronic kidney disease (CKD) with erythropoietin-stimulating agents (ESAs) is commonplace. The optimal hemoglobin treatment target has not been established. A clearer understanding of the health-related quality of life (HQOL) impact of hemoglobin target levels is needed. We systematically reviewed the randomized controlled trial (RCT) data on HQOL for patients treated with low to intermediate (9.0-12.0 g/dL) and high hemoglobin target levels (>12.0 g/dL) and performed a meta-analysis of all available 36-item short-form (SF-36) RCT data. METHODS: We conducted a search to identify all RCTs of ESA therapy in patients with anemia associated with CKD (1966-December 2006). Inclusion criteria were (1) 30 or more participants, (2) anemic adults with CKD, (3) epoetin (alfa and beta) or darbepoetin used, (4) a control arm, and (5) reported HQOL using a validated measure. All available SF-36 data underwent meta-analysis using the weighted mean difference. RESULTS: Of 231 full texts screened, 11 eligible studies were identified. The SF-36 was used in 9 trials. Reporting of these data was generally incomplete. Data from each domain of the SF-36 were summarized. Statistically significant changes were noted in the physical function (weighted mean difference [WMD], 2.9; 95% confidence interval [CI], 1.3 to 4.5), general health (WMD, 2.7; 95% CI, 1.3 to 4.2), social function (WMD, 1.3; 95% CI, -0.8 to 3.4), and mental health (WMD, 0.4; 95% CI, 0.1 to 0.8) domains. None of the changes would be considered clinically significant. CONCLUSIONS: Our study suggests that targeting hemoglobin levels in excess of 12.0 g/dL leads to small and not clinically meaningful improvements in HQOL. This, in addition to significant safety concerns, suggests that targeting treatment to hemoglobin levels that are in the range of 9.0 to 12.0 g/dL is preferred.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".