Nomogram to estimate the activity of second-line therapy for advanced urothelial carcinoma (UC).
Bibliographic record
Abstract
4524 Background: Prognostic factors may impact on endpoints used in phase II trials of second-line therapy for advanced UC. We aimed to study the impact of prognostic factors (liver metastasis [LM], anemia [Hb<10 g/dl], ECOG-performance status [PS] ≥1, time from prior chemotherapy [TFPC]) on PFS6 and RR. Methods: Twelve phase II trials evaluating second-line chemotherapy and/or biologics (n=748) in patients with progressive disease were pooled. PFS was defined as tumor progression or death from any cause. PFS6 was defined from the date of registration and calculated using the Kaplan-Meier method. RR was defined using RECIST 1.0. A nomogram predicting PFS6 was constructed using the RMS package in R (www.r-project.org). Results: Data regarding progression, Hb, LM, PS and TFPC were available from 570 patients. The mean age was 65.1 years, 45.3% had ECOG-PS ≥1, 30.2% had LM, 14.6% had anemia and TFPC was <6 months (mo) in 60.2%. The overall median PFS was 2.7 mo, PFS6 was 22.2% (95% CI: 18.8-25.9) and RR was 17.5% (95% CI: 14.5%-20.9%). For every unit increase in risk group, the hazard of progression increased by 41% and the odds of response decreased by 48% (Table). A nomogram was constructed to predict PFS6 on an individual patient level. Conclusions: PFS6 and RR vary as a function of prognostic factors in patients receiving second-line therapy for advanced UC. A nomogram incorporating prognostic factors might facilitate the evaluation of activity across phase II trials enrolling heterogeneous populations and can help to select and stratify patients for phase III evaluation of suitable agents. [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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".