Impact of bone metastasis on clinical outcomes in metastatic urothelial bladder cancer (mUC) for patients treated with first-line platinum-based chemotherapy.
Bibliographic record
Abstract
431 Background: Prognostic nomograms have identified patients with mUC less likely to benefit from treatment. Yet, analyses have relied on grouped patient variables (ie: bone plus visceral metastases) and refinement is needed. This study aims to evaluate the clinical outcomes of bone metastases (BM) on patients with mUC treated with first-line platinum-based chemotherapy. Methods: Patients receiving first-line platinum-based chemotherapy for mUC at the Princess Margret Cancer Centre from 12/2009-10/2016 were selected from central pharmacy records. Patient and tumor characteristics, in addition to treatment history were collected retrospectively. Oncologist assessed best response (OABR; complete [CR] or partial [PR] response; stable (SD] or progressive [PD] disease) was evaluated by chart review. Progression-free (PFS) and overall survival (OS) were estimated by Kaplan-Meier (log-rank). Overall response rate (ORR) was calculated from OABR. Multivariable analysis (MVA) was performed using Cox-regression. Significance was evaluated at the p < 0.05 level. Results: Of 112 patients receiving platinum-based chemotherapy, 62 (55%) had mUC. Most patients were male (73%), had ECOG PS of 0-1 (71%), and de novo metastases (23%). Patients received a median of 5 chemotherapy cycles (range 1-8), and median follow-up was 12.7 months. Number of organs involved with metastases varied (1 = 32%; 2 = 45%; 3+ = 23%); with lymphnode (77%), bone (37%), and lung metastases (34%) frequently involved. Median PFS (months) was shorter (p < 0.05) in patients with BM (5.6; 95%CI:2.7-7.0) compared to no BM (8.0; 95%CI:6.5-9.5). Median OS (months) was shorter (p < 0.05) in patients with BM (8.9; 95%CI:7.2-15.9) compared to no BM (16.1; 95%CI:10.5-23.4). ORR was lower for those with BM (22%) compared to no BM (56%). MVA using BM and prognostic nomogram criteria showed significance for ECOG 2+ (HR = 2.72; p < 0.05) with a trend towards significance for BM (HR = 1.85; p = 0.06). Conclusions: mUC patients with BM have lower response and inferior survival by log-rank. This could have direct implications for clinical decision making and stratification of factors in clinical trials.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".