Systemic therapy for unresectable and metastatic transitional cell carcinoma of the urothelium: first-line and beyond
Bibliographic record
Abstract
PURPOSE OF REVIEW: The review aims to provide an overview of recent advances and future research direction in the management of patients with advanced transitional cell carcinoma. RECENT FINDINGS: Early data of the randomized phase III study comparing paclitaxel, cisplatin, and gemcitabine with gemcitabine plus cisplatin for advanced urothelial cancer detected no survival difference. A phase II study investigated the safety and efficacy of trastuzumab, carboplatin, gemcitabine, and paclitaxel in human epidermal growth factor receptor-2/neu-positive advanced urothelial carcinoma and reported promising results. Renal-sparing regimens are under active development. A nonrandomized comparison of the 3-week with the 4-week schedule for gemcitabine and cisplatin showed that the 3-week schedule had less hematological toxicity and better dose intensity. Potential molecular markers such as excision repair cross-complementation group 1, emmprin, and survivin for survival and/or platinum resistance in patients with transitional cell carcinoma showed promise. SUMMARY: Recent data do not support change in the current standard of care for advanced transitional cell carcinoma. Clinical testing of emerging anticancer therapies using new agents, new combinations, and new approaches is under active investigation. Rational combination and new strategy in clinical trial design are critical for new drug development for transitional cell carcinoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".