Impact of kidney transplantation on serum bone mineral levels and anemia – a cohort study on Egyptian children
Bibliographic record
Abstract
Introduction Disorder of bone mineral indicators and anemia are frequent morbidities in children with end stage kidney disease (ESKD) that can persist after kidney transplantation (KT). This study aims to investigate the effect of KT on bone minerals and anemia by comparing their levels before KT and at regular follow-up intervals after KT. Material and methods A cohort of 30 pediatric kidney transplant recipients (KTRs) was followed up at 3-month intervals for their first post-transplantation year. Review of pre-transplantation, transplantation- related data and regular measurement of serum calcium, phosphorus, alkaline phosphatase (ALP), parathyroid hormone (PTH), hemoglobin, hematocrit, iron and ferritin was performed. Results Serum phosphorus and PTH levels significantly decreased after KT (p < 0.001) with no significant change in calcium (p = 0.221) or ALP (p = 0.377) levels. Frequency of hyperparathyroidism significantly decreased after KT (p < 0.05) but 53.3% and 63.3% of patients had hyperparathyroidism at 6 and 12 months respectively. Parathyroid hormone did not show a significant difference at 6- and 12-month assessments after transplantation (p = 0.82). Hemoglobin level, hematocrit and serum iron significantly increased after KT (p = 0.019, 0.048 and 0.008 respectively). Frequency of phosphorus level abnormalities and anemia significantly declined after KT (p < 0.001 and 0.0356 respectively). Parathyroid hormone levels positively correlated with glomerular filtration rate (p = 0.004 and CC = 0.608). Patients on tacrolimus had less phosphorus than those on cyclosporine at 12 months (p = 0.005). Conclusions Successful KT in children partially normalizes bone mineral disorders accompanied with ESKD by reducing serum phosphorus levels. Hyperparathyroidism is prevalent by the end of the first post-transplantation year. Anemia does still exist after KT but to a lesser extent than pre-transplantation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".