Anemia management in chronic kidney disease
Bibliographic record
Abstract
Anemia is common in chronic kidney disease (CKD) due to a state of erythropoietin deficiency. Erythropoietin therapy has been used for approximately 20 years to correct anemia in CKD and to improve both subjective and objective outcomes. Guidelines that establish a hemoglobin (Hb) goal for anemia correction in CKD patients are largely based on observational data. Controversy still exists, however, because outcomes have not been consistent with various degrees of anemia correction. The number of prospective randomized trials investigating the effects of anemia correction on cardiovascular (CV) morbidity and mortality in CKD patients, an already high-risk group, is limited. With respect to improving CV outcomes in the CKD population, the currently available trial data caution against raising Hb levels in CKD patients to approach more "normal" physiologic ranges. The disappointing experience with the trial data must be weighed against the beneficial associations of erythropoietin therapy that have been generated from observational data. Establishing the ideal target Hb ranges for anemia correction in CKD patients remains a dynamic process and leaves many gray areas to be further elucidated. Here, we present a case that underscores the need to consider the study design when reviewing the data at a population level in order to determine what is most appropriate for our patient.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".