Unusual Pattern of Dyslipidemia in Children Receiving Steroid Minimization Immunosuppression after Renal Transplantation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Corticosteroids are an important contributor to posttransplant hyperlipidemia. Since 2004, we have used a steroid minimization immunosuppression protocol. This study investigated the effect of steroid minimization on dyslipidemia in pediatric renal allograft recipients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Children (<18 years) who underwent renal transplants at our center from January 2001 to January 2008 were studied. Data analyzed included age, gender, race, body mass index, cholesterol, triglyceride, LDL cholesterol, HDL cholesterol, and steroid dose. Data between the cohorts receiving maintenance steroids and steroid-minimization were compared using multivariable analyses. The primary outcome measures were the prevalence of, and the effect of steroid use, on dyslipidemia. RESULTS: Twenty-nine patients were studied. Sixteen were receiving maintenance steroids, and 13 were on a steroid minimization regimen. Mixed effects analysis of covariance models demonstrated that at 1 month, children receiving maintenance steroids had higher cholesterol compared with the steroid minimization group. Statistically significant differences in total cholesterol were not seen at other time points. Similar findings were noted for the LDL cholesterol, LDL/HDL, and cholesterol/HDL ratios. At 1 month, the serum HDL cholesterol was substantially lower in the steroid minimization group. Differences in the HDL cholesterol levels remained significant throughout the first year. CONCLUSIONS: Steroid use is a significant independent risk factor for hypercholesterolemia during the first post-transplant month. The significance of lower HDL cholesterol among patients receiving steroid minimization needs further study and may be cause for concern.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".