The Practical Implications of Using Estimated GFR as the Presumed Reference Variable to Estimate Transplant Chronic Kidney Disease
Bibliographic record
Abstract
We determined the proportions of matched kidney transplant isotope GFRs (iGFRs) to the estimated functions (eGFRs) calculated from Isotope Dilution Mass Spectrometry (IDMS), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), and Cockcroft-Gault (CG) equations. One thousand four hundred and three iGFR/eGFR pairs on 390 kidney transplant patients were compared considering the iGFR or eGFR as the reference or test variable. Conformity of iGFR to CG estimates demonstrated the least bias of 1.3±18.4 mL/min/1.73 m2 (compared to 1.5±19.4 and - 2.2±19.2 for IDMS and CKDI-EPI, P<0.05) and CKD-EPI estimates the highest precision of 4.1±41.8 (compared to 11.3±43.9 for IDMS and 5.7±37.3 for CG; P<0.05). IDMS eGFR cut off less than 60 and less than 30 mL/min/1.73m2 were correctly matched by iGFR in 79.4% and 49.1% of the times, while CKD-EPI was matched by iGFR in 83.5% and 52.5%. CG was matched in 78.3% and 53.6%. IGFR cut off levels of less than 60, and less than 30 mL/min/1.73m2 were predicted by IDMS in 83.8% and 64.0% of the times. CKD-EPI was correct in 77.8% and 59.0% and CG in 82.5% and 41.6%, respectively. Transplant eGFR results obtained by CKD-EPI or CG are likely to be more precise and less biased than IDMS.
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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 | 0.075 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".