Prednisone withdrawal in pediatric kidney transplant recipients on tacrolimus‐based immunosuppression: Four‐year data
Bibliographic record
Abstract
Corticosteroids have been used in renal transplant immunosuppression for over 40 yr. Despite their adverse effects, steroid therapy continues to be part of early as well as maintenance immunosuppression in most pediatric renal transplant centers. The association of steroids with growth retardation, weight gain, and acne may be particularly distressing during the critical years of adolescence and young adulthood, increasing the risk of medication non-adherence. This study reviews the outcomes of pediatric renal transplant patients treated with low-dose tacrolimus, mycophenolate mofetil, or azathioprine, and planned prednisone withdrawal. Thirty-seven pediatric renal transplant recipients were withdrawn from steroids. The mean follow-up after steroid withdrawal was 42+/-19 months. Graft and patient survival were 100%. The mean serum creatinine levels and calculated creatinine clearances remained stable throughout the period of observation. The mean creatinine clearance was 96+/-24 mL/min/1.73 m2 at steroid withdrawal and 93+/-20 mL/min/1.73 m2 at the latest follow-up. Five patients restarted prednisone; in four (11%) it was for suspected or confirmed acute rejection. Improvements were observed in serum lipid profiles, blood pressure, and body mass index. Most patients experienced catchup or stable growth after prednisone withdrawal. Four patients developed viral infections; all were successfully treated. The potential benefits of steroid withdrawal in pediatric renal transplantation are supported by our results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".