The Sask Formula to Estimate Glomerular Filtration Rate in Renal Transplant Patients
Bibliographic record
Abstract
The aim of this study was to develop a glomerular filtration rate (GFR) equation for renal transplant and compare its performance with Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and isotope dilution mass spectrometry (IDMS) equations. Using genetic symbolic regression analysis, the Sask equation was developed from a training sample of 772 isotope GFR (iGFR) scans performed in 99 transplanted patients. It was then validated in two other samples of 269 scans with the same number of patients. Standard methods including accuracy at 30% range from reference values were compared. In two validation samples, the Sask equation maintained the lowest bias of -0.7 ± 19.0 and 0.4 ± 18.4 ml/min/1.73 m(2) (p < 0.05) versus -3.1 ± 19.6 and -7.2 ± 18.8 ml/min/1.73 m(2) for CKD-EPI and -2.2 ± 19.2 and -6.5 ± 18.3 ml/min/1.73 m(2) for IDMS, respectively. In those with iGFR between 90 and 30 ml/min/1.73 m(2), the Sask equation demonstrated: (1) the lowest bias of -1.0 ± 15.7 and -0.4 ± 15.7 ml/min/1.73 m(2) (p < 0.05 vs. other tests); (2) an accuracy of 75.5 and 76.1% (p < 0.05 vs. other tests), and (3) a mean percentage error of 1.9 ± 30.5 and -4.1 ± 31.4 ml/min/1.73 m(2) (p < 0.05 vs. other tests). Analysis based on gender demonstrated improved performance in the total and subtotal female populations with GFR between 90 and 30 ml/min/1.73 m(2). The CKD-EPI and Sask equations performed better than IDMS. The Sask equation demonstrated improved bias over CKD-EPI, with iGFR between 90 and 30 ml/min/1.73 m(2), particularly in females.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".