How I Do It: Maintenance avelumab for advanced urothelial carcinoma
Bibliographic record
Abstract
For more than four decades, platinum-based chemotherapy regimens have served as the established standard-of-care for advanced urothelial carcinoma (aUC). However, advancements in our understanding of cancer biology and tumor microenvironment have reshaped the therapeutic landscape and prognosis of this incurable disease. Immune checkpoint inhibitors (ICIs) that target programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) are firmly established tools in aUC management, leading to enhanced life span and improved quality of life for patients. In patients who achieved stable disease or better following platinum-based chemotherapy, maintenance therapy with the PD-L1 antibody avelumab significantly enhanced overall survival (OS) by approximately 7 months compared to best supportive care in the phase 3 JAVELIN Bladder 100 trial. As a result, avelumab received FDA approval in June 2020 as a maintenance therapy for aUC patients treated with first-line platinum-based chemotherapy. Therefore, aUC care plans should incorporate maintenance avelumab into standard first-line treatment regimens for these patients. The objective of this brief article is to provide insight into the utilization of avelumab, identify patients who may benefit from this treatment, and review the methodology, advantages, potential side effects and their management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".