Update on the Assessment of GFR in Patients with Cancer
Bibliographic record
Abstract
Accurate assessment of GFR is key in patients with cancer to guide drug eligibility, adjust dosing of systemic therapy, and minimize the risks of undertreatment and systemic toxicity. Several aspects of GFR evaluation in patients with cancer have been unclear, such as the choice of the GFR estimating equation and the overall lack of data on the reliability of new filtration markers, such as cystatin C. This uncertainty has led to concerns that inaccurate GFR estimation may have a large effect on clinical practice and research. Recent data have brought important developments to the field. The new and timely Kidney Disease Improving Global Outcomes 2024 Clinical Practice Guideline for the Evaluation and Management of CKD raised important considerations and provided guidance on key aspects of GFR evaluation in patients with cancer. The guidelines cover valid estimating equations, incorporation of cystatin C in GFR estimation, drawbacks of using race in GFR estimation, and acknowledge that non-GFR determinants of filtration markers may be prominent in patients with cancer, reducing the accuracy of GFR estimating equations, prompting greater utilization of GFR measurement. The aim of this review is to summarize advances in GFR evaluation in patients with cancer considering the new Kidney Disease Improving Global Outcomes guidelines and other recent data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".