Should Hemoglobin be Normalized in Patients with Chronic Kidney Disease?
Bibliographic record
Abstract
In the last decade the nephrology community has learned much about the impact of anemia on patients with kidney disease. Therapy of anemia can correct many of the symptoms which seriously compromise patient function. Despite the obvious benefits, controversy continues regarding the optimal target hemoglobin concentration both in patients prior to dialysis and in dialysis populations. In this editorial we review the clinical data that contribute to this controversy and the physiologic concepts underlying the treatment of anemia. Furthermore, we discuss the need to individualize hemoglobin targets for specific patient populations and the importance of early identification and treatment of anemia in patients with kidney disease. The economic impact of normalizing hemoglobin with the use of erythropoietin and intravenous or oral iron has affected clinical practice over the last decade. Current guidelines published by Kidney Disease Outcomes and Quality Initiative (KDOQI), the European Working Group on Anemia Management, and the Canadian Society of Nephrology all recommend target hemoglobin concentrations and thresholds for initiation of therapy and also suggest the need for reevaluation of current targets in light of new evidence. This editorial supports those guidelines and challenges the reader to critically evaluate current practice in the context of the accumulating data and the physiologic principles discussed herein. The therapy of anemia in patients with chronic kidney disease (CKD) is becoming increasingly sophisticated and is an essential component of care in patients with CKD. However, the effects of therapy will be most impressive when accompanied by the optimal care of all hemodynamic and metabolic abnormalities that are associated with CKD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".