Decreased bone mineral density in the pediatric renal transplant population
Bibliographic record
Abstract
All renal transplant recipients at our centre have had bone mineral density assessment (BMD) by DEXA scans of their lumbar spine while on the transplant waitlist and at 6-month intervals post-transplant over the past 7 yr. Risk factors for osteopenia and osteoporosis including donor source, dialysis status prior to transplantation, prior renal disease, and biopsy confirmed rejection events and their relationship to BMD of the lumbar spine were assessed. Thirty-nine children transplanted over the past 7 yr were included in this study. In total, 127 BMD longitudinal assessments were performed. From 1990 to 1997, ATG/ALG was used as antibody induction therapy. From 1997 to 2002, Basiliximab was utilized. Cyclosporin A (CyA) was the primary immunosuppressant for most children with tacrolimus as primary (n = 2) and switch for CyA failure or toxicity (n = 16). Prednisone was administered at a dose of 1 mg/kg/day for the first week and tapered to 10 mg/m2/alternate day by 1 month post-transplant. Azathioprine 1.5 mg/kg/day was continued for 1 yr and discontinued in children who were rejection free. All rejections were biopsy confirmed and treated with a prednisone pulse. Using a repeated measures regression analysis, we have found that L1-L4 BMD z score is affected by height and transplant number. It is also related to time relative to transplant in a quadratic fashion. There was an inverse relationship between advancing patient age and L1-L4 BMD z score. L1-L4 BMD z score was not related to weight, pre-existing renal disease, gender, donor source, type of renal replacement therapy prior to transplantation, or rejection events.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".