Weight and Height Changes and Factors Associated With Greater Weight and Height Gains After Pediatric Renal Transplantation: A NAPRTCS Study
Bibliographic record
Abstract
BACKGROUND.: Obesity is a significant problem among children undergoing renal transplantation. We sought to describe changes in adiposity (reflected by percent difference from the median body mass index [BMI] for height-age and sex [BMI%]) after pediatric renal transplantation and to identify risk factors for greater gains in adiposity within 12 months of transplantation and for persistence of these gains at 48 months. The changes in height-for-age were also examined. METHODS.: By using the North American Pediatric Renal Trials and Collaborative Studies registry, we performed a retrospective cohort study of children (age 2-18 years.) transplanted between 1995 and 2006, and followed up to January 2007. Multivariable linear regression was used to identify risk factors for greater gains in adiposity and height. RESULTS.: BMI was recorded at baseline in 4326 children, and collected every 6 months. Median BMI% increased by 11.37 units within 6 months; no substantial changes were seen thereafter. The pattern of change in BMI% was similar regardless of BMI% at transplant. Age 6 to 12 years at transplant, more remote transplant, female sex, black race, Hispanic ethnicity, and lower baseline BMI% were associated with significantly greater gains in adiposity both within 12 months and persisting 48 months posttransplant. Compared with daily use, no corticosteroid use at 6 and 48 months were associated with smaller increases in BMI% within the first 12 months and at 48 months, respectively. CONCLUSIONS.: The majority of children experienced early increases in BMI%, which persisted up to 4 years. Increases in BMI% were similar regardless of BMI% at baseline.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".