Risk factors and model for predicting toxicity‐related treatment discontinuation in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor–targeted therapy: Results from the International Metastatic Renal Cell Carcinoma Database Consortium
Bibliographic record
Abstract
BACKGROUND: Vascular endothelial growth factor (VEGF)-targeted therapies are standard treatment for metastatic renal cell carcinoma (mRCC); however, toxicities can lead to drug discontinuation, which can affect patient outcomes. This study was aimed at identifying risk factors for toxicity and constructing the first model to predict toxicity-related treatment discontinuation (TrTD) in mRCC patients treated with VEGF-targeted therapies. METHODS: The baseline characteristics, treatment outcomes, and toxicity data were collected for 936 mRCC patients receiving first-line VEGF-targeted therapy from the International Metastatic Renal Cell Carcinoma Database Consortium. A competing risk regression model was used to identify risk factors for TrTD, and it accounted for other causes as competing risks. RESULTS: Overall, 198 (23.8%) experienced TrTD. Sunitinib was the most common VEGF-targeted therapy (77%), and it was followed by sorafenib (18.4%). The median time on therapy was 7.1 months for all patients and 4.4 months for patients with TrTD. The most common toxicities leading to TrTD included fatigue, diarrhea, and mucositis. In a multivariate analysis, significant predictors for TrTD were a baseline age ≥60 years, a glomerular filtration rate (GFR) <30 mL/min/1.73 m(2) , a single metastatic site, and a sodium level <135 mmol/L. A risk group model was developed that used the number of patient risk factors to predict the risk of TrTD. CONCLUSIONS: In the largest series to date, age, GFR, number of metastatic sites, and baseline sodium level were found to be independent risk factors for TrTD in mRCC patients receiving VEGF-targeted therapy. Based on the number of risk factors present, a model for predicting TrTD was built to be used as a tool for toxicity monitoring in clinical practice.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".