Estimating Glomerular Filtration Rate in Kidney Transplantation
Bibliographic record
Abstract
Accurate measurement of GFR is critical for the evaluation of new therapies and the care of renal transplant recipients. Although not accurate in renal transplantation, GFR is often estimated using creatinine-based equations. Cystatin C is a marker of GFR that seems to be more accurate than creatinine. Equations to predict GFR based on the serum cystatin C concentration have been developed, but their accuracy in transplantation is unknown. GFR was estimated using four equations (Filler, Le Bricon, Larsson, and Hoek) that are based on serum cystatin C and seven equations that are based on serum creatinine in 117 adult renal transplant recipients. GFR was measured using radiolabeled diethylenetriaminepentaacetic acid (99mTc-DTPA), and the bias, precision, and accuracy of each equation were determined. The mean (99m)Tc-DTPA GFR was 58 +/- 23 ml/min per 1.73 m(2). The cystatin C-based equations of Filler and Le Bricon had the lowest bias (-1.7 and -3.8 ml/min per 1.73 m2), greatest precision (11.4 and 11.8 ml/min per 1.73 m2), and highest accuracy (87 and 89% within 30% of measured GFR, respectively). The cystatin C equations remained accurate even when the measured GFR was >60 ml/min per 1.73 m2. The creatinine-based equations were not as accurate, with only 53 to 80% of estimates within 30% of measured GFR. Cystatin C-based equations are more accurate at predicting GFR in renal transplant recipients than traditional creatinine-based equations. Further prospective studies with repetitive measurement of cystatin C are needed to determine whether cystatin C-based estimates of GFR will be sufficiently accurate to monitor long-term allograft function.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".