Cancer drugs and acute kidney injury: new therapies and new challenges
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cancer therapies continue to evolve at a rapid pace and although novel treatments, including immunotherapies and targeted therapies have allowed for substantial improvements in cancer survival, they carry associated risks of acute kidney injury (AKI). We aim to summarize the existing literature on AKI associated with the spectrum of systemic cancer treatments, including conventional chemotherapies, newer immunotherapies, and the growing number of targeted cancer therapies, which may be associated with both AKI and 'pseudo-AKI'. RECENT FINDINGS: Conventional cytotoxic chemotherapies (e.g. cisplatin and other platinum-based agents, methotrexate, pemetrexed, ifosfamide, etc.) with well recognized nephrotoxicities (predominantly tubulointerstitial injury) remain in widespread use. Immunotherapies (e.g., immune checkpoint inhibitors and CAR-T therapies) may be associated with kidney immune-related adverse events, most often acute interstitial nephritis, and rarely, glomerular disease. Recently, multiple targeted cancer therapies have been associated with reduced renal tubular secretion of creatinine, causing elevations in serum creatinine and apparent 'pseudo-AKI'. To complicate matters further, these agents have had biopsy-proven, 'true' kidney injury attributed to them in numerous case reports. SUMMARY: Clinicians in nephrology and oncology must be aware of the various potential kidney risks with these agents and recognize those with clinically meaningful impact on both cancer and kidney outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".