Assessment of kidney function: clinical indications for measured GFR
Bibliographic record
Abstract
In the vast majority of cases, glomerular filtration rate (GFR) is estimated using serum creatinine, which is highly influenced by age, sex, muscle mass, body composition, severe chronic illness and many other factors. This often leads to misclassification of patients or potentially puts patients at risk for inappropriate clinical decisions. Possible solutions are the use of cystatin C as an alternative endogenous marker or performing direct measurement of GFR using an exogenous marker such as iohexol. The purpose of this review is to highlight clinical scenarios and conditions such as extreme body composition, Black race, disagreement between creatinine- and cystatin C-based estimated GFR (eGFR), drug dosing, liver cirrhosis, advanced chronic kidney disease and the transition to kidney replacement therapy, non-kidney solid organ transplant recipients and living kidney donors where creatinine-based GFR estimation may be invalid. In contrast to the majority of literature on measured GFR (mGFR), this review does not include aspects of mGFR for research or public health settings but aims to reach practicing clinicians and raise their understanding of the substantial limitations of creatinine. While including cystatin C as a renal biomarker in GFR estimating equations has been shown to increase the accuracy of the GFR estimate, there are also limitations to eGFR based on cystatin C alone or the combination of creatinine and cystatin C in the clinical scenarios described above that can be overcome by measuring GFR with an exogenous marker. We acknowledge that mGFR is not readily available in many centres but hope that this review will highlight and promote the expansion of kidney function diagnostics using standardized mGFR procedures as an important milestone towards more accurate and personalized medicine.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".