Impact of CKD Stages Based on Measured vs. Estimated Glomerular Filtration Rate on the Overall Survival of Patients with Cancer
Bibliographic record
Abstract
Background: Data assessing the impact of chronic kidney disease (CKD) stages on the overall survival (OS) of cancer patients are largely retrospective and rely on the serum level of creatinine (SCr) to estimate the glomerular filtration rate (eGFR). We aimed to evaluate the impact of different CKD stages on the OS of patients with cancer admitted for treatment using measured GFR (mGFR) and eGFR. Methods: This is a prospective cohort of adult patients with solid tumors (diagnosed in the last 90 days) initiating treatment at the Sao Paulo State Cancer Institute. mGFR was determined by plasma clearance of 51Cr-EDTA. eGFR was calculated using the 2021 CKD-EPI equations, based on SCr (eGFrcr), Scys (eGFRcys), and the combined version (eGFRcrcys). CKD stages were classified according to the KDIGO guidelines using mGFR, eGFRcr, eGFRcys, and eGFRcrcys. Results: A group of 1,011 patients recruited from April 2015 to September 2017 were included for analysis and censored in March 2023. Patients were 50.6 ±13 y, 50.7% female. The most common cancer sites were breast (22.4%), gastrointestinal (21.9%), and male genital (21.2%); 15.5% had metastasis. ECOG 0, 1, and 2&3 comprised 64%, 32%, and 6% of patients, respectively. Time of follow-up was 5.7 (2.5-6.5) y; overall mortality was 27.4%. In the adjusted Cox regression model, stage 3b CKD based on mGFR and eGFRcys and stages 3a and 3b based on eGFRcys were associated with worse OS. CKD stages based on eGFRcr were not associated with OS (Table). Conclusion: This is the first study assessing the impact of CKD stages on the OS of patients with solid tumors incorporating mGFR and SCys. Our results endorse the utility of eGFRcys and mGFR as valuable instruments in the kidney care of cancer patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".