Glomerular filtration rate as a putative ‘surrogate end‐point’ for renal transplant clinical trials in children
Bibliographic record
Abstract
Only with prospective randomized controlled trials is it possible to evaluate the several immunosuppressive regimens available to renal allograft recipients. Commonly used surrogate markers of clinical outcome, such as patient and graft survival, are constantly improving. Current immunosuppressive protocols have improved 1-yr graft survival to over 90%. The small differences in graft survival among the various immunosuppressive regimes require large patient cohorts in order to establish statistical significance. Such studies are often difficult to conduct in a timely manner, particularly in children. This necessitates the search for better surrogate markers sensitive enough to detect differences in smaller cohorts and in a shorter period of time. While the degree of fibrosis in transplant biopsies might well predict long-term graft survival, protocol biopsies are expensive, invasive, and unpopular among clinicians. In native kidneys, glomerular filtration rate (GFR) closely correlates with disease progression and interstitial fibrosis and appears to be well positioned as a less invasive surrogate marker for long-term outcome. Nonetheless, the ideal marker for GFR remains obscure. Serum creatinine has several major drawbacks, making it a poor predictor of GFR. This review discusses the several methods used to estimate or measure GFR with emphasis on 125I-iothalamate clearance and serum cystatin C (cys-C). Of all the serum markers, cys-C is the most reliable and the most promising. However, cys-C and other endogenous markers cannot replace the diagnostic sensitivity and reliability of radiolabeled markers of GFR such as 125I-iothalamate in renal transplant clinical trials. Unfortunately, clearance of most radiolabeled markers of GFR including 125I-iothalamate remain costly and time consuming.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".