New 6-factor prognostic model for patients (pts) with advanced urothelial carcinoma (UC) receiving post-platinum atezolizumab.
Bibliographic record
Abstract
413 Background: Prognostic factors for overall survival (OS) have been identified in pts receiving post-platinum chemotherapy for advanced UC, but it is unknown whether these factors and/or others optimally predict OS for pts treated with PD1/PD-L1 inhibitor therapy. Methods: Pt level data from two UC salvage trials evaluating atezolizumab were used: IMvigor210 (n = 310) for training and PCD4989g (n = 95) for validation. Univariable and multivariable Cox regression analyses were performed to evaluate the association of the prognostic factors recognized in the chemotherapy setting (ECOG performance status [ECOG-PS], liver metastasis (LM), anemia, treatment-free interval, albumin), neutrophil-lymphocyte ratio (NLR), eosinophil count, platelet count (PLT), site of primary/metastases, stage at diagnosis, smoking, LDH, prior therapies and immune cell PD-L1 status by IHC with OS. Clinical factors were dichotomous and lab values normalized by logarithmic transformation as needed. Stepwise selection was employed to propose an optimal model using the training dataset; pts were then categorized by number of risk factors. Concordance, discrimination (c-statistic) and calibration were assessed in the validation dataset using bootstrap analyses. Results: The factors included in the optimal prognostic model for OS were: ECOG-PS 1 vs. 0 (HR 1.64 [95% CI: 1.20, 2.24], p = 0.002), LM (1.45 [1.08, 1.94], p = 0.014), PLT (1.73 [1.14, 2.61], p = 0.010), NLR (1.84 [1.45, 2.34], p < 0.001), LDH (1.54 [1.19, 1.99], p = < 0.001) and anemia (HR = 1.60 [1.17, 2.21] p = 0.004). The c-statistic was 0.690 (95% CI = 0.649-0.715) and 0.759 (0.694-0.795) in the training and validation datasets, respectively. 1-year OS of pts in the training and the validation cohorts were similar. PD-L1 score was statistically significant when adjusted for the optimal model, but did not improve clinical interpretability (c-statistic = 0.698). Conclusions: A new validated 6-factor prognostic model for OS including ECOG-PS, LM, PLT, NLR, LDH and anemia is proposed in the setting of post-platinum atezolizumab for advanced UC. Applicability of the model to other PD1/PD-L1 inhibitors and PD-L1 IHC assays warrant investigation.
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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 | 0.004 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".