The effects of discontinuing cinacalcet at the time of kidney transplantation
Bibliographic record
Abstract
Background. The calcimimetic, cinacalcet, is approved for treating secondary hyperparathyroidism (SHPT) in patients with chronic kidney disease (CKD) on dialysis. Biochemical profiles and clinical outcomes in patients discontinuing cinacalcet at kidney transplantation have not been previously described.Methods. We performed a retrospective observational study evaluating post-transplant biochemical profiles and clinical outcomes in patients who had enrolled in phase 2 or 3 randomized, placebo-controlled studies of cinacalcet before receiving a kidney transplant.Results. The study included 28 former cinacalcet and 10 former placebo patients. Post-kidney transplant, there were no obvious differences between the two groups in levels of serum intact parathyroid hormone, calcium or phosphorus. One patient in each group underwent post-transplant parathyroidectomy. Kidney transplant failure was apparent in one former cinacalcet-treated patient (4%) and three former placebo patients (30%). The duration of hospitalization (mean +/- standard error) immediately post-transplant in these two groups was 2.3 +/- 0.3 and 3.4 +/- 0.8 weeks, respectively.Conclusions. Using cinacalcet to treat SHPT in patients with CKD awaiting kidney transplantation does not appear to modify SHPT-related post-transplant biochemical profiles, or clinical outcomes, compared with placebo.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".