Risk factors and a model to predict toxicity-related treatment discontinuation in patients with metastatic renal cell carcinoma treated with VEGF-targeted therapy: Results from the International Metastatic RCC Database Consortium.
Bibliographic record
Abstract
464 Background: VEGF targeted therapy (VEGF-TT) are standard in advanced metastatic renal cell carcinoma (mRCC), however, toxicities that can lead to drug discontinuation can have a significant impact on patient (pt) outcomes. We aimed to identify risk factors for toxicity and develop the first model to predict toxicity-related treatment discontinuation (TrRD) in mRCC pts treated with VEGF-TT. Methods: Baseline characteristics and treatment outcome data were collected on 936 mRCC pts on first-line VEGF-TT from 7 IMDC institutions. TrTD was analyzed using a competing risk regression model for treatment discontinuation. Results: Median follow up was 23 months. Treatment discontinuation occurred in 833 pts (89%), of which 198 (23.8%) were related to drug toxicity. Sunitinib was the most common VEGF-TT (77%) in our series followed by sorafenib (18.4%). Median time on therapy was 7.1 months in all pts and 4.4 months for pts with TrTD. Most common toxicities leading to TrTD included fatigue, diarrhea and mucositis. On multivariate analysis, significant adverse predictors for TrTD (p<0.05) were: age (≥60 years), baseline glomerular filtration rate (GFR) <30 cc/min, number of metastatic sites (>1), and baseline sodium level (
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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 | 0.013 |
| 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) | 0.002 | 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".