Anti VEGF-TKI Treatment and New Renal Adverse Events Not Reported in Phase III Trials
Bibliographic record
Abstract
Cabozantinib and lenvatinib have been approved for the treatment of progressive medullary thyroid cancer and radioiodine-resistant thyroid cancer, respectively. Both phase III trials of cabozantinib and lenvatinib reported that renal adverse events (AEs) rarely occurred. The cabozantinib phase III study reported no AEs related to renal toxicity. In the lenvatinib phase III trial grade 3 (CTCAE), proteinuria (urinary protein ≥3.5 g/24 h) was found in 10.0% of the lenvatinib and 0.0% of the placebo patients. We report a 23-year-old patient with metastatic medullary thyroid cancer who was enrolled in the phase III trial, comparing cabozantinib to placebo and a 67-year-old patient with metastatic, papillary thyroid carcinoma who was undergoing treatment with lenvatinib during his enrollment in the phase III trial. The first patient had a normal kidney function initially, but developed end-stage chronic kidney disease unexpectedly on cabozantinib and additional zoledronate infusion. Whereas the second patient suffered from a dramatic aggravation of his known mild chronic renal insufficiency (KDOQI stage 2) due to long standing hypertension and atherosclerosis during the treatment with lenvatinib. These severe AEs due to anti-VEGF tyrosine kinase inhibitor treatment were unknown so far. In conclusion, these 2 cases argue for increased awareness for the possibility of renal failure as a consequence of anti-VEFG treatment. Predisposing conditions like known mild chronic renal insufficiency with only mild proteinuria and with atherosclerosis or precipitating co-medications like zoledronate infusion need to be accounted for to prevent these severe AEs.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".