Health impact of using anti-PD-(L)1 agents to treat early-stage cancers in Switzerland: a modeling study
Bibliographic record
Abstract
Background: Inhibitors of programmed cell death protein 1 (PD-1) and its ligand (PD-L1) (referred to hereafter as anti-PD-(L)1 agents) are approved to treat a variety of advanced-stage cancers. Incorporating these agents into neoadjuvant/adjuvant treatment regimens for early-stage cancers may provide health and economic benefits at the population level. Methods: A health outcomes projection model compared two scenarios in Switzerland: I) anti-PD-(L)1 agents used only for advanced/metastatic disease, and II) anti-PD-(L)1 agents starting in the neoadjuvant/adjuvant setting. The model focused on three cancers for which anti-PD-(L)1 agents are currently approved in Europe in early stages: melanoma, renal cell carcinoma (RCC), and triple-negative breast cancer (TNBC), projecting clinical evolution over 10 years. Estimated outcomes included life-years, quality-adjusted life-years (QALYs), recurrences/events, active treatments for metastatic disease, adverse events, and deaths. Results: Of the estimated 10,659 eligible patients during 2022-2031, 9,050 were predicted to initiate neoadjuvant and/or adjuvant treatment with anti-PD-(L)1 agents for treatment of melanoma, RCC, or TNBC. Compared to anti-PD-(L)1 agents being available only in the metastatic setting, use of anti-PD-(L)1 agents in the neoadjuvant and/or adjuvant setting for these 3 cancers was projected to avoid 1,144 recurrences (a 27% decrease), prevent 1,577 active treatments in the metastatic setting (a 35% decrease), avoid 530 deaths (a 23% decrease), and increase life-years without recurrence by 3,416 (a 10% increase). Conclusion: The use of anti-PD(L)1 agents to treat early-stage cancers in Switzerland is anticipated to result in better outcomes by preventing recurrences/events, active metastatic treatments, and deaths.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".