Bone Density and Cortical Structure after Pediatric Renal Transplantation
Bibliographic record
Abstract
The impact of renal transplantation on trabecular and cortical bone mineral density (BMD) and cortical structure is unknown. We obtained quantitative computed tomography scans of the tibia in pediatric renal transplant recipients at transplantation and 3, 6, and 12 months; 58 recipients completed at least two visits. We used more than 700 reference participants to generate Z-scores for trabecular BMD, cortical BMD, section modulus (a summary measure of cortical dimensions and strength), and muscle and fat area. At baseline, compared with reference participants, renal transplant recipients had significantly lower mean section modulus and muscle area; trabecular BMD was significantly greater than reference participants only in transplant recipients younger than 13 years. After transplantation, trabecular BMD decreased significantly in association with greater glucocorticoid exposure. Cortical BMD increased significantly in association with greater glucocorticoid exposure and greater decreases in parathyroid hormone levels. Muscle and fat area both increased significantly, but section modulus did not improve. At 12 months, transplantation associated with significantly lower section modulus and greater fat area compared with reference participants. Muscle area and cortical BMD did not differ significantly between transplant recipients and reference participants. Trabecular BMD was no longer significantly elevated in younger recipients and was low in older recipients. Pediatric renal transplant associated with persistent deficits in section modulus, despite recovery of muscle, and low trabecular BMD in older recipients. Future studies should determine the implications of these data on fracture risk and identify strategies to improve bone density and structure.
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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 | 0.003 |
| 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".