Measurement Error in Estimated GFR Slopes across Transplant Chronic Kidney Disease Stages
Bibliographic record
Abstract
BACKGROUND: This study examines if transplant glomerular filtration rate (GFR) slope prediction is affected by the degree of transplant chronic kidney disease (CKDT) stage. METHODS: Serial changes in estimated GFR (DeltaeGFR) by Cockcroft-Gault (CG) and Modified Diet in Renal Disease-Isotope Dilution Mass Spectrometry (MDRD-IDMS) equations were compared to simultaneous changes in isotope GFR (DeltaiGFR) in renal transplant patients who had at least four scans. RESULTS: Total number of patients (iGFR scans) was 99 (772) while the corresponding numbers in CKDT stages 1-4 were 33 (103), 69 (239), 75 (316) and 37 (96), respectively. Measurement error [(DeltaeGFR - DeltaiGFR) x 100/DeltaiGFR] (median +/- IQR, interquartile range) estimated from CG and MDRD-IDMS slopes were -414.29 +/- 276.16% and -342.86 +/- 210.18% (stage 1); -350.00 +/- 301.22% and -300.00 +/- 525.00% (stage 2); -26.02 +/- 404.38% and -26.58 +/- 423.13% (stage 3); 10.26 +/- 142.18% and -76.92 +/- 145.64% (stage 4), respectively. The proportion of patients with CG measurement error < or =1-fold in stages 1 and 2 of 12 and 14.5% was significantly (p < 0.05) lower than that of 36.3 and 52.8% at stages 3 and 4, respectively. Similar measurement errors were observed for MDRD-IDMS. CONCLUSIONS: Transplant GFR slope prediction is affected by the degree of renal dysfunction. Errors in slope prediction are much higher in those with better function and thus add another limitation for eGFR use in longitudinal studies on progressive graft dysfunction.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".