Practice recommendations for the monitoring of renal function in pediatric non‐renal organ transplant recipients
Bibliographic record
Abstract
The management of non-renal pediatric solid organ transplant recipients has become complex over the last decade with innovations in immunosuppression and surgical techniques. Post-transplantation follow-up is essential to ensure that children have functioning allografts for as long as possible. CKD is highly prevalent in these patients, often under recognized, and has a profound impact on patient survival. These practice recommendations focus on the early detection and management of hypertension, proteinuria, and renal dysfunction in non-renal pediatric solid organ transplant recipients. We present seven practice recommendations. Renal function should be monitored regularly in organ transplant recipients, utilizing assessment of serum creatinine and cystatin C. GFR should be calculated using the new Schwartz formula. Transplant physicians should also monitor blood pressure using automated oscillometric devices and confirm repeated abnormal measures with manual blood pressure readings and ambulatory 24-h blood pressure monitoring. Proteinuria and microalbuminuria should also be assessed regularly. Referrals to a pediatric nephrologist should be made for non-renal organ transplant recipients with repeated blood pressures >95th percentile using the Fourth Task Force reference intervals, microalbumin/creatinine ratio >32.5 mg/g (3.7 mg/mmol) creatinine on repeated testing and/or GFR <90 mL/min/1.73 m(2) .
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".