Single Drug Therapy of PD-1/PD-L1 Checkpoint Inhibitors for Advanced Urothelial Bladder Cancer
Bibliographic record
Abstract
Since the first approval of Atezolizumab in May 2016, immunotherapy, PD-1/PD-L has completely changed the way bladder cancer is treated, as chemotherapy was the sole available choice as a treatment for bladder cancer, and the results still was not optimistic, with five licensed drugs treating bladder cancer. Despite the generally poor prognosis of advanced bladder cancer, some patients show persistent responses to immune checkpoint inhibitors. This review summarizes the efficacies and safety of the five drugs: Durvalumab, Atezolizumab, Avelumab and other drugs - from different studies respectively for treating advanced bladder cancer and mentions the side effects and future perspectives. For the treatment, all inhibitors that was licensed have akin efficacy and safety traits, but they differ in terms of dosage, frequency, and financial burden. Only Pembrolizumab, to date, has revealed advantage over conventional chemotherapy in a stochastic Phase III scenario. Pembrolizumab and Atezolizumab are also well tolerated and approved for patients who are unable to receive cisplatin treatment. Patients with bladder cancer now have some hope, thanks to immunotherapy. The current environment is continuously evolving, and new immunotherapy-combination trials are being conducted to further enhance results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".