Hypercalcemia in renal transplant patients: prevalence and management in Canadian transplant practice
Bibliographic record
Abstract
Hypercalcemia, occurring in up to 25% of patients within 12 months following renal transplantation, and persistent hyperparathyroidism were evaluated following renal transplantation, by retrospective chart review of 1000 adult patients transplanted between January 1, 2003 and January 31, 2008 with at least six months follow-up. Serum calcium, parathyroid hormone, and phosphate levels were recorded at 12, 24, 36, and 48 months. Average follow-up was 766 (535) d (mean (SD); median 668 d). Majority were first transplants (85%); deceased donor 57%. Point prevalence of hypercalcemia (serum Ca(2+) > 2.6 mM) was 16.6% at month 12, 13.6% at month 24, 9.5% at month 36, and 10.1% at month 48. Point prevalence of serum parathyroid hormone (PTH) > 10 pM was 47.6% at month 12, 51.1% at month 24, 43.4% at month 36, and 39.3% at month 48. Estimated glomerular filtration rate (GFR) was maintained throughout and was not different between patients with or without hypercalcemia or elevated PTH. Cinacalcet was prescribed in 12% of patients with hypercalcemia and persistent hyperparathyroidism; parathyroidectomy was performed in 112/1000 patients, 15 post-transplant. Persistent hyperparathyroidism, often accompanied by hypercalcemia, is common following successful renal transplantation, but the lack of clear management suggests the need for further study and development of evidence-based guidelines.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".