Improved parathyroid hormone control by cinacalcet is associated with reduction in darbepoetin requirement in patients with end-stage renal disease
Bibliographic record
Abstract
BACKGROUND: Uncontrolled hy-per-parathyroidism causes bone marrow fibrosis, leading to erythropoietin (EPO) resistance. Medical treatment with cinacalcet is effective in reducing plasma parathyroid hormone (PTH) levels, but its effect on darbepoetin dosing is unknown. METHODS AND AIMS: We conducted a retrospective cohort study of 40 end-stage renal disease (ESRD) patients (age: 55 ± 14; mean ± SD; 21:male) who had at least 12 months of cinacalcet therapy. The distribution of renal replacement therapies were: 14 peritoneal dialysis, 18 conventional hemodialysis and 8 nocturnal hemodialysis. Standard dialysis related biochemical indices and medications used were recorded. The primary objective of the study was to ascertain the difference in darbepoetin responsiveness before and after 12 months of cinacalcet therapy. Our secondary objective was to determine if there was a relationship between the changes in PTH and darbepoetin requirement. RESULTS: Overall, PTH levels decreased from 197.5 (151.8; 249.2) to 66.1 (41.2; 136.5) (median (25th;75th percentile)) pmol/l; p < 0.001. Cinacalcet dose increased from 30.0 ± 6 to 63 ± 25 mg/day, p < 0.05. Hemoglobin remained unchanged (116 ± 13 to 116 ± 13 g/l), while darbepoetin requirement decreased from 40 (20; 60) to 24 (19; 59) μg/week, p = 0.02. The remainder of the dialysis-related biochemistry (electrolytes, calcium, phosphate, iron status) and vitamin D use remained unchanged. A reduction in PTH level of greater than 30% was experienced by 82.5% (33/40) of our cohort. Among the responders, the fall in PTH and reduction darbepoetin requirement were related (R = -0.48, p = 0.004). CONCLUSIONS: Reduction of PTH by cinacalcet is associated with a decrease in darbepoetin requirement. The interface between bone and bone marrow in uremia represents a critical step in red blood cell production which merits further investigation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".