Modelling cost‐effectiveness of a biomarker‐based approach to neoadjuvant chemotherapy for muscle‐invasive bladder cancer
Bibliographic record
Abstract
OBJECTIVES: To model the cost-effectiveness of a biomarker-based approach to select patients for neoadjuvant chemotherapy (NAC) before radical cystectomy (RC) in muscle-invasive bladder cancer (MIBC). PATIENTS AND METHODS: We obtained data from the most recent clinical studies on patients with locally advanced MIBC treated by RC, including stage distributions, overall survival (OS) estimates, associated costs, and utilisation/response to NAC. Additionally, we estimated the putative efficacy of three biomarkers to select patients for NAC: DNA-repair gene panel [ataxia telangiectasia mutated (ATM), retinoblastoma 1 (RB1), and Fanconi anaemia complementation group C (FANCC)], excision repair cross-complementation group 2 (ERCC2), and ribonucleic acid (RNA) subtypes. A decision analysis model was developed to evaluate the cost-effectiveness of biomarker-based approaches to select patients with MIBC for NAC. Comparison of cost-effectiveness included RC alone, unselected NAC plus RC, and NAC based on the three aforementioned biomarkers. RESULTS: The DNA-repair gene panel-based approach to NAC was the most cost-effective strategy (mean OS of 3.14 years, $31 482/life year). Under this approach, 38% would undergo NAC, about twice the number of patients who are currently receiving NAC for MIBC. Such an approach would improve mean OS by 5.2, 1.6, and 4.4 months compared to RC alone, a hypothetical scenario where all patients received NAC, and compared to current estimates of NAC utilisation, respectively. CONCLUSIONS: A biomarker-based strategy to identify patients with MIBC who should undergo NAC was more cost-effective than unselected use of NAC or RC alone. As further data becomes available, such a model may serve as a basis for incorporating biomarkers into clinical decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".