Avelumab First-Line Maintenance for Advanced Urothelial Carcinoma: Results From the JAVELIN Bladder 100 Trial After ≥2 Years of Follow-Up
Bibliographic record
Abstract
Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned coprimary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported. Initial results from the phase III JAVELIN Bladder 100 trial (ClinicalTrials.gov identifier: NCT02603432 ) showed that avelumab first-line (1L) maintenance plus best supportive care (BSC) significantly prolonged overall survival (OS) and progression-free survival (PFS) versus BSC alone in patients with advanced urothelial carcinoma (aUC) who were progression-free after 1L platinum-containing chemotherapy. Avelumab 1L maintenance treatment is now a standard of care for aUC. Here, we report updated data with ≥ 2 years of follow-up in all patients, including OS (primary end point), PFS, safety, and additional novel analyses. Patients were randomly assigned 1:1 to receive avelumab plus BSC (n = 350) or BSC alone (n = 350). At data cutoff (June 4, 2021), median follow-up was 38.0 months and 39.6 months, respectively; 67 patients (19.5%) had received ≥2 years of avelumab treatment. OS remained longer with avelumab plus BSC versus BSC alone in all patients (hazard ratio, 0.76 [95% CI, 0.63 to 0.91]; 2-sided P = .0036). Investigator-assessed PFS analyses also favored avelumab. Longer-term safety was consistent with previous analyses; no new safety signals were identified with longer treatment duration. In conclusion, longer-term follow-up continues to show clinically meaningful efficacy benefits with avelumab 1L maintenance plus BSC versus BSC alone in patients with aUC. An interactive visualization of data reported in this article is available.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.002 |
| 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".