Discontinuing VEGF-targeted therapy (VEGF-TT) for progression versus toxicity impacts outcomes of second-line therapies in metastatic renal cell carcinoma (mRCC).
Bibliographic record
Abstract
503 Background: A significant minority of mRCC patients prematurely discontinue first-line (1L) VEGF-TT due to toxicity. Whether clinical outcomes differ in patients receiving second line (2L) TT based on reason for discontinuation of 1L are unknown. Methods: Patients from 15 International mRCC Database Consortium (IMDC) centers who started 2L TT were included and the reason for discontinuation of 1L collected. Treatment outcomes of 2L, including response, time to treatment failure (TTF) and overall survival (OS) were assessed. Results: 1124 patients were identified: 866 patients (77%) discontinued 1L VEGF-TT due to progression, 208 patients (19%) due to toxicity. 50 patients (4%) who discontinue due to other reasons were excluded. The reason for discontinuation of 1L did not differ by IMDC risk group at start of 1L VEGF-TT (p=0.54) or 2L therapy (p=0.52). Median time from 1L VEGF-TT initiation to start of 2L in patients who progressed or stopped prematurely due to toxicity was 9.8 and 7.9 months, respectively. Compared to patients who stopped 1L VEGF-TT due to progression, patients who stopped due to toxicity had longer drug free interval between 1L and 2L (1.4 vs. 0.7 months; p<0.001), greater clinical benefit (CR/PR/SD) in second line (68% vs. 56%; adjusted OR: 1.58 (95%CI:1.07,2.35), p=0.023) and longer OS (17.4 vs. 11.2 months; adjusted HR: 0.69 (0.56,0.84), p=0.0002) adjusted for type of therapy, time to initiation of 2L, IMDC risk group and number of metastases at 2L (Table). Conclusions: mRCC patients with VEGF TT discontinuation 1L due to toxicity have better outcomes with 2L therapy than patients who stop therapy because of progression. These findings should be taken in consideration when designing clinical trials for second-line therapies in mRCC. [Table: see text]
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".