Clinical practice guidelines on iron therapy: A critical evaluation
Bibliographic record
Abstract
Anemia is common among patients with chronic kidney disease (CKD) and it is managed primarily with erythropoiesis-stimulating agents (ESA) and iron therapy. Following concerns around ESA therapy and economic constraints, IV iron is more and more administered worldwide. Several guidelines or position papers, which give indications on iron therapy in CKD patients, have been issued in Nephrology. Unfortunately, the field is characterized by a lack of evidence. As a result, the recommendations/suggestions are not uniform. There is general consensus to prescribe iron therapy to patients who are clearly iron deficient. In addition, iron therapy may increase Hb values, delay the start of ESA therapy in ESA-naïve patients and reduce ESA dose in ESA-treated patients. However, there is debate on the safety and efficacy of IV iron therapy when given in the presence of already high serum ferritin levels. In addition, not all the guidelines/position papers differentiate between non-dialysis/dialysis patients and between the presence/absence of ESA therapy. Many international Bodies or Societies suggest caution when administering IV iron during infections. A trial of oral iron should be considered as a first step, especially in the ND-CKD population. Finally, recommendations on the prevention of anaphylactic reactions following IV iron therapy are given by several bodies. There is consensus that IV iron is to be administered in the presence of resuscitative facilities (including medications) and personnel trained for emergencies.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".