Management of Anemia in Erythropoietin-Resistant Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Human recombinant erythropoietin (rHuEPO) is administered to patients with end-stage renal disease for treatment of anemia. OBJECTIVE: To assess the impact of a structured team approach to anemia management in rHuEPO-resistant hemodialysis patients. METHODS: This was an 8-month prospective, open-label, quality-improvement initiative. Nineteen patients in a 160-bed hemodialysis unit receiving rHuEPO doses >300 units/kg/wk were defined as rHuEPO-resistant. Hemoglobin (Hb), iron indices, parathyroid hormone, folate, B12, aluminum, and reticulocyte counts were determined at baseline. The former 3 parameters were followed every 6, 12, and 26 weeks, respectively. Vascular access flow was regularly assessed via ultrasonic dilution methodology. Target Hb was 12.0-13.5 g/dL. All factors potentially contributing to rHuEPO resistance were assessed and, if possible, treated every 6 weeks by a dedicated anemia team. Downward rHuEPO dosage adjustments of 12.5-25% to the closest 1000 units were considered if underlying causes of rHuEPO resistance could not be identified or reversed, or if the Hb rose beyond the target level. RESULTS: Dysfunctional vascular access and iron deficiency were the predominant treatable factors associated with rHuEPO resistance. At 8 months, mean rHuEPO dosage decreased significantly from 469 to 319 units/kg/wk (p < 0.001) and mean Hb increased significantly from 10.6 to 11.6 g/dL (p = 0.023). Eight-month cost savings approximated $45 000 (CDN$). CONCLUSIONS: A structured team approach to the management of rHuEPO-resistant patients was successful in significantly lowering rHuEPO dosage with improvement in serum Hb at a substantial cost savings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".