The Health Impact of Using Anti-PD-1 Agents to Treat Early-Stage Cancer in Belgium
Bibliographic record
Abstract
INTRODUCTION: Anti-PD-1 agents, inhibitors of programmed cell death protein 1 (PD-1), significantly improve clinical outcomes and overall survival for individuals with several metastatic and early-stage cancers. This study evaluates the health impact of using anti-PD-1 agents for early-stage disease (ESD) treatment of melanoma (stage IIB-C and III), renal cell carcinoma (RCC), and triple-negative breast cancer (TNBC) in Belgium (2023-2032). METHODS: Belgian individuals eligible for ESD treatment (target population) entered a Markov-based health outcomes model in a recurrence/event/disease-free state. The model compared anti-PD-1 agents only for metastatic disease treatment (reference scenario) versus anti-PD-1 agents for ESD treatment (ESD scenario) from 2023 to 2032. Clinical outcomes of the model included recurrence/event/disease-free life-years (LYs), total LYs, quality-adjusted LYs (QALYs), recurrences/events, active treatments for metastatic disease, and total deaths. The cumulative health impact of ESD anti-PD-1 treatment in Belgium was calculated as the difference in health outcomes between the ESD and reference scenarios for the time horizon. RESULTS: Of the 14,306 eligible individuals, 11,065 were predicted to initiate treatment with anti-PD-1 agents for ESD. Anti-PD-1 therapies for ESD increased recurrence/event/disease-free LYs (+13.4%), total LYs (+4.4%), and QALYs (+4.9%) after 10 years. Additionally, ESD treatment decreased recurrences/events (-24.6%), active treatments for metastatic disease (-28.6%), and total deaths (-23.8% decrease) over 10 years. CONCLUSIONS: The investment in and use of innovative anti-PD-1 agents for the treatment of early-stage cancers would have a positive health impact for Belgium and align with the high standards in cancer care called for in Europe's Beating Cancer Plan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".