How to monitor renal function in pediatric solid organ transplant recipients
Bibliographic record
Abstract
The aim is to review the tools for early detection of renal dysfunction after pediatric solid organ transplantation. Currently, the most widely used marker for detection of renal dysfunction involves measurement of GFR. Inulin clearance forms the "gold standard" method for measuring GFR; however, nuclear medicine methods ((51)Cr EDTA and (99)Tc DTPA isotope clearance studies) have replaced inulin clearance. The measurement of serum creatinine has a low sensitivity for the early detection of renal damage. The Schwartz formula using patient height and serum creatinine requires center-specific constants and has limitations associated with creatinine determination. These limitations may be overcome using a cystatin C-based GFR estimation. In diabetic nephropathy, and more recently in hemolytic uremic syndrome, microalbuminuria has been established as a useful screening tool for renal damage, while its predictive value in the transplantation setting needs to be established. All transplant recipients should be screened for hypertension. Early referral for ambulatory 24-h blood pressure monitoring and involvement of pediatric nephrologists should be considered. All pediatric solid organ transplant recipients receiving CNI should be screened regularly for high blood pressure and early evidence of renal damage using either GFR scans or cystatin C-based GFR estimations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".