Survival Outcomes Associated with First and Second-Line Palliative Systemic Therapies in Patients with Metastatic Bladder Cancer
Bibliographic record
Abstract
Background: Real-world data on palliative systemic therapies (PST) in treating metastatic bladder cancer (mBC) is limited. This study investigates current trends in treating mBC with first- (1L) and second-line (2L) chemotherapy (CT) and immunotherapy (IT). Methods: A chart review was conducted on patients diagnosed with stage II-IV bladder cancer in 2014–2016. Survival outcomes were compared between chemotherapy, immunotherapy, and supportive care. Results: out of 297 patients, 77% were male. 44% had stage IV disease at diagnosis. Median age at metastasis was 73 years. 40% of patients received 1L PST and 34% received 2L PST. Median overall survival (mOS) was longer in those receiving PST versus no treatment (p < 0.001). Patients receiving CT and IT sequentially had the longest mOS (18.99 months). First-line IT and CT mOS from treatment start dates were 5.03 and 9.13 months, respectively (p = 0.81). Gemcitabine with cisplatin (8.88 months) or carboplatin (9.13 months) were the most utilized 1L chemotherapy regimens (p = 0.85). 2L IT and CT mOS from treatment start dates were 6.72 and 3.78 months, respectively (p = 0.15). Conclusion: real-world mOS of >1.5 years in mBC is unprecedented and supports using multiple lines of PST. Furthermore, immunotherapy may be a comparable alternative to chemotherapy in both 1L and 2L settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".