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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".