Five-Factor Prognostic Model for Survival of Post-Platinum Patients with Metastatic Urothelial Carcinoma Receiving PD-L1 Inhibitors
Bibliographic record
Abstract
PURPOSE: A prognostic model for overall survival of post-platinum patients with metastatic urothelial carcinoma receiving PD-1/PD-L1 inhibitors is necessary as existing models were constructed in the chemotherapy setting. MATERIALS AND METHODS: Patient level data were used from phase I/II trials evaluating PD-L1 inhibitors following platinum based chemotherapy for metastatic urothelial carcinoma. The derivation data set consisted of 2 phase I/II trials evaluating atezolizumab (405). Two phase I/II trials that evaluated avelumab (242) and durvalumab (198) comprised the validation data sets. Cox regression analyses evaluated the association of candidate prognostic factors with overall survival. Stepwise selection was used to select an optimal model using the derivation data set. Discrimination and calibration were assessed in the avelumab and durvalumab data sets. RESULTS: The 5 prognostic factors identified in the optimal model using the atezolizumab derivation data set were ECOG-PS (1 vs 0, HR 1.80, 95% CI 1.36-2.36), liver metastasis (HR 1.55, 95% CI 1.20-2.00), platelet count (HR 2.22; 95% CI 1.54-3.18), neutrophil-to-lymphocyte ratio (HR 1.94, 95% CI 1.57-2.40) and lactate dehydrogenase (HR 1.60, 95% CI 1.28-1.99). There was robust discrimination of survival between low, intermediate and high risk groups. The c-statistic was 0.692 in the derivation and 0.671 and 0.773 in the avelumab and durvalumab validation data sets, respectively. A web based interactive tool was developed to calculate the expected survival probabilities based on risk factors. CONCLUSIONS: A validated 5-factor model has satisfactory prognostic performance for survival across 3 PD-L1 inhibitors to treat metastatic urothelial carcinoma after platinum therapy and may assist in stratification, interpreting and designing trials incorporating PD-1/PD-L1 inhibitors in the post-platinum setting.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".