Patterns of split-dose gemcitabine and cisplatin (GC) use in patients with locally advanced or metastatic urothelial carcinoma (la/mUC): Results of a 10-country physician survey.
Bibliographic record
Abstract
699 Background: Gemcitabine + cisplatin (GC) is a highly effective treatment for la/mUC. For patients who are ineligible for standard GC, split-dose GC is a promising alternative. This study aimed to provide insights into the use of split-dose GC and factors influencing treatment choice across 10 countries. Methods: Between January and March 2024, an international cross-sectional study was conducted via an online survey of physicians treating patients with la/mUC in Australia, Brazil, Canada, France, Germany, India, Italy, Spain, the UK, and the US. Demographics, practice patterns, and clinical parameters influencing treatment choice were collected. Multivariate logistic regression was used to determine factors influencing the use of split-dose GC. Results: The study included 791 physicians, mostly male (73%), who had a mean age of 43 years and an average of 13.2 years in practice, with 59% in academic practice. Most (85%) reported using split-dose GC across different treatment settings, including in 41% in neoadjuvant treatment, 40% in adjuvant treatment, and 43% in the metastatic setting. Physicians with the following characteristics were more likely to use split-dose GC: longer time in practice (per 10 years: odds ratio [95% CI], 1.58 [1.18-2.16]), higher patient volume (per 10 patients: 1.06 [1.01-1.14]), and public vs private practice (1.94 [1.27-2.97]). The preferred schedule in la/mUC was GC 35 mg/m 2 on days 1+8 of a 21-day cycle (57%). Most physicians (80%) used avelumab maintenance treatment after split-dose GC in patients without progression. The table shows acceptable lower thresholds for creatinine clearance (CrCl) reported by physicians for patients receiving split-dose GC. Conclusions: This large international survey underscores the extensive use of split-dose GC as an alternative regimen for patients with la/mUC who are ineligible for standard GC. Physicians who are more experienced and manage more patients are more likely to use split-dose GC. The results highlight the need for evaluating split-dose GC prospectively and establishing consensus guidelines for optimal use of split-dose GC in clinical practice and clinical trials. Physiciansn=635 CrCl in patients with ECOG performance status 0-1, n (%) 50 mL/min 573 (90.2) 40 mL/min 407 (64.1) 30 mL/min 195 (30.7) CrCl in patients with ECOG performance status 2, n (%) 50 mL/min 443 (69.8) 40 mL/min 290 (45.7) 30 mL/min 119 (18.7)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".