The treatment of anemia in chronic kidney disease: understandings in 2006
Bibliographic record
Abstract
PURPOSE OF REVIEW: Anemia is a well recognized complication of chronic kidney disease and is associated with significant morbidity. It is important for clinical care to identify appropriate treatments and targets for hemoglobin. This review describes current understandings of the treatment of anemia using the most recent published articles. RECENT FINDINGS: Numerous studies, including observational and randomized control trials, of varying sizes and using both surrogate and hard outcomes have been published. On balance, there is little to support normalization of hemoglobin in the chronic kidney disease population. While some studies have described harm, there are some issues related to overinterpretation based on study trial reporting. The treatment of anemia can be successfully achieved with the use of oral or intravenous iron and erythropoiten-stimulating agents. Caution should be exercised when treating those with significant cardiovascular morbidity, and those who require very high doses of erythropoiten-stimulating agents to achieve normal hemoglobin. SUMMARY: Large observational population-based studies continue to demonstrate the association of low hemoglobin with adverse outcomes, and randomized control trials fail to show a benefit of normalized hemoglobin. Anemia therapy does improve quality of life. In the current era of aggressive chronic kidney disease management, it does not appear that anemia therapy attenuates left ventricular growth or changes cardiovascular outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".