Impact of response to prior chemotherapy (RTPC) on outcomes in second-line therapy for advanced urothelial carcinoma (UC): Implications for trial design.
Bibliographic record
Abstract
4539 Background: Performance status (PS), hemoglobin (Hb), liver metastasis (LM), and time from prior chemotherapy (TFPC) are significant prognostic factors in second-line therapy for advanced UC. Setting of prior chemotherapy, i.e., metastatic or perioperative, has not appeared significant. However, the impact of prior chemosensitivity is unclear, which may confound trial interpretation. Hence, we examined the prognostic impact of RTPC, when prior therapy was given for metastatic disease. Methods: Six phase II trials evaluating second-line chemotherapy and/or biologics (n=504) were pooled. Patients who received prior therapy for metastatic disease were eligible for analysis if data regarding Hb, LM, PS, and TFPC were available. Response by RECIST to first-line therapy was recorded. Progression-Free Survival (PFS) and overall survival (OS) were calculated from the date of registration using the Kaplan-Meier method. Results: 275 pts were evaluable for analysis. Patients received gemcitabine-paclitaxel, cyclophosphamide-paclitaxel, pazopanib, docetaxel plus vandetanib/placebo or vinflunine (2 trials). Those with prior response (n=111) had a median (95% CI) OS of 8.0 (6.8-9.4) months (mo) and PFS of 3.0 (2.6-4.0), compared with OS and PFS of 5.9 (5.0-6.6) mo and 2.6 (2.0-2.8) for those without prior response (n=164). Multivariable analysis did not reveal an independent impact of RTPC on PFS or OS (Table). Conclusions: RTPC in patients receiving prior chemotherapy for metastatic disease did not confer an independent prognostic impact with second-line therapy for advanced UC. Patients who received prior chemotherapy in peri-operative or metastatic settings may be enrolled in the same second-line trial stratified for PS, anemia, LM and TFPC. [Table: see text]
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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 | 0.050 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".