Height‐adjusted lean body mass and its associations with physical activity and kidney function in pediatric kidney transplantation
Bibliographic record
Abstract
Abstract Background Although LBM is positively associated with health outcomes, studies assessing determinants for the accrual of ht‐LBM, such as physical activity, are limited. This study aimed to assess ht‐LBM levels in pediatric kidney transplant recipients and test its association with baseline and contemporaneous variables, including physical activity. Methods A retrospective cross‐sectional review was performed on 46 pediatric kidney transplant recipients, and a longitudinal review was performed on a subset of recipients with serial post‐transplant (n = 21) and pre/post‐transplant (n = 11) ht‐LBM measurements. Ht‐LBM measurements were obtained using DXA scans. Results This cohort was 16.0 (IQR 12.3, 17.7) years old, 56.5% male and 46 ± 45 months post‐transplant with a mean ht‐LBM of 15.1 ± 2.5 kg/m2. A median ht‐LBM increase of 1.6 kg/m2 (IQR − 0.1, 2.6 kg/m2; p < .01) was observed, over 29.2 ± 12.0 months from the earliest post‐transplant scan obtained at 46 ± 25 months post‐transplant until the most recent post‐transplant scan. A 1.7 ± 1.4 kg/m2 (p < .01) increase was observed between pre‐ and post‐transplant DXA scans which were taken at 12 ± 11 months pre‐transplant and 13 ± 6 months post‐transplant, respectively. In separate adjusted models, lower physical activity questionnaire scores (n = 17, beta = 1.55, p = .02), faster rate of estimated glomerular filtration rate decline (beta = 0.05, p < .048) adjusted for annualized change in BSA, and younger age at scan (beta = 0.32, p < .01) were each significant predictors of lower ht‐LBM. Conclusions Physical activity and kidney function may influence ht‐LBM in the pediatric kidney transplant population.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".