Low hemoglobin at hemodialysis initiation: an international study of anemia management and mortality in the early dialysis period
Bibliographic record
Abstract
BACKGROUND: Anemia at hemodialysis (HD) initiation is common. Correcting low hemoglobin (Hgb) before HD initiation may improve survival by avoiding potential harms of chronic anemia, high doses of erythropoiesis-stimulating agents (ESAs) and intravenous (IV) iron in the early HD period, and/or rapid Hgb rise. METHODS: We included 4604 incident HD patients from 21 countries in the Dialysis Outcomes and Practice Patterns Study Phases 4-5 (2009-15). Because low Hgb at HD start may reflect comorbidity or ESA hyporesponse, we restricted our analysis to the 80% of patients who achieved Hgb ≥10 g/dL 91-120 days after HD start (Month 4). RESULTS: About 53% of these patients had Hgb <10 g/dL in Month 1 (<30 days after HD start); they were younger with a similar comorbidity profile (versus Hgb ≥10 g/dL). Month 1 Hgb was associated with first-year HD mortality (adjusted hazard ratio for 1 g/dL higher Hgb was 0.89; 95% confidence interval: 0.81-0.97), despite minimal differences in Month 4 Hgb. Patients with lower Hgb in Month 1 received higher doses of ESA, but not IV iron, over the first 3 months of HD. Results were consistent when excluding catheter users or adjusting for IV iron and ESA dose over the first 3 months. CONCLUSIONS: Even among patients with Hgb ≥10 g/dL 3 months later, anemia at HD initiation was common and associated with elevated mortality. A more proactive approach to anemia management in advanced chronic kidney disease (CKD) may thus improve survival on HD, though long-term prospective studies of non-dialysis CKD patients are needed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".