Trimodal therapy vs. radical cystectomy for muscle-invasive bladder cancer: A Canadian cost-effectiveness analysis
Bibliographic record
Abstract
INTRODUCTION: Trimodal therapy (TMT) is a suitable alternative to neoadjuvant chemotherapy (NAC) and radical cystectomy (RC) for patients with muscle-invasive bladder cancer (MIBC). In this study, we conducted a cost-effectiveness evaluation of RC±NAC vs. TMT for MIBC in the universal and publicly funded Canadian healthcare system. METHODS: We developed a Markov model with Monte-Carlo microsimulations. Rates and probabilities of transitioning within different health states (e.g., cure, locoregional recurrence, distant metastasis, death) were input in the model after a scoped literature review. Two main scenarios were considered: 1) academic center; and 2) populational-level. Results were reported in life-years gained (LYG), quality-adjusted life years (QALY), and incremental cost-effectiveness ratio (ICER). A sensitivity analysis was performed. RESULTS: A total of 20 000 patients were simulated. For the academic center model, TMT was associated with increased effectiveness (both in LYG and QALY) at a higher cost compared to RC±NAC at five and 10 years. This resulted in an ICER of $19 746/QALY per patient undergoing the TMT strategy at 10 years of followup. For the populational-level model, RC±NAC was associated with higher effectiveness at 10 years, with an ICER of $3319/QALY per patient. This study was limited by heterogeneity within the studies used to build the model. CONCLUSIONS: In this study, TMT performed in academic centers was cost-effective compared to RC±NAC, with higher effectiveness at a higher cost. On the other hand, RC±NAC was considered cost-effective compared to TMT at the populational-level. Further studies are needed to confirm these results.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".