A nomogram including baseline prognostic factors to estimate the activity of second‐line therapy for advanced urothelial carcinoma
Bibliographic record
Abstract
OBJECTIVE: To study the impact of the prognostic factors liver metastasis (LM), anaemia (haemoglobin [Hb] <10 g/dL), Eastern Cooperative Oncology Group performance status (ECOG-PS) ≥1 and time from previous chemotherapy (TFPC) on the activity of second-line therapy for advanced urothelial carcinoma (UC). PATIENTS AND METHODS: Twelve phase II trials evaluating second-line chemotherapy and/or biological characteristics (n = 748) in patients with progressive disease were pooled. Progression-free survival (PFS) was defined as tumour progression or death from any cause. The PFS rate at 6 months (PFS6) was defined from the date of registration and calculated using the Kaplan-Meier method. Response rate (RR) was defined using Response Evaluation Criteria in Solid Tumours (RECIST) 1.0. A nomogram predicting PFS6 was constructed using the rms software package in R (http://www.r-project.org). RESULTS: Data regarding progression, anaemia, LM, ECOG-PS and TFPC were available from 570 patients in nine phase II trials. The overall median PFS was 2.7 months, PFS6 was 22.2% (95% confidence interval 18.8-25.9) and the RR was 17.5% (95% CI: 14.5-20.9%). For every unit increase in risk group, the hazard of progression in 6 months increased by 41% and the odds of response decreased by 48%. A nomogram was constructed to predict PFS6 on an individual patient level. The model was internally validated and was shown to have acceptable calibration performance. CONCLUSIONS: The RR and PFS6 vary as a function of baseline prognostic factors in patients receiving second-line therapy for advanced UC. A nomogram incorporating prognostic factors facilitates the evaluation of outcomes across phase II trials enrolling heterogeneous populations and helps select suitable agents for phase III testing.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".