Bibliographic record
Abstract
Anemia is one of the main comorbidities related to chronic kidney disease (CKD). Until the advent of erythropoiesis stimulating agents (ESA), endogenous erythropoietin deficiency has been thought to be the main culprit of anemia in CKD patients. The use of ESAs has shed new light on the physiology of CKD anemia, where iron homeostasis plays an increasingly important role. Disorders of iron homeostasis occurring in CKD turn the anemia management in those patients into a complex multifactorial therapeutic task, where ESA and Iron dose must be properly balanced to achieve the desired outcome without exposing the patients to the risk of serious adverse events. This review covers diagnostic markers traditionally used for quantifying iron status in CKD patients, such as serum ferritin and transferrin saturation, new ones, such as reticulocyte hemoglobin content and percent hypochromic red cells (HRC), as well as experimental ones, such as hepcidin and soluble transferrin receptor (sTfR). Each marker is presented in terms of their diagnostic performance, followed by biological and analytical variability data. Advantages and disadvantages of each marker are briefly discussed. Although serum ferritin and transferrin saturation are easily available, they exhibit large biological variability and require caution when used for diagnosing iron status in CKD patients. Reticulocyte hemoglobin content and the percentage of HRC are more powerful, but their widespread use is hampered by the issue of sample stability in storage. sTfR and hepcidin show promise, but require further investigation as well as the development of standardized, low-cost assay platforms.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".