Impact on health outcomes and productive work of adding anti-PD-1 agents to treat early-stage cancers in the United Kingdom: a modelling study
Bibliographic record
Abstract
Background: Anti-PD-1 agents are recommended in adjuvant or perioperative settings in some early-stage cancers. The health and productivity benefits of anti-PD-1 use on a population level, however, are unknown. Methods: A decision model was developed to quantify the health and productivity outcomes of adding anti-PD-1 agents to traditional management strategies in the adjuvant or perioperative setting for melanoma stage IIB/IIC/III, triple negative breast cancer, and renal cell carcinoma in the United Kingdom. The model consisted of four separate Markov models and compared outcomes in two scenarios: one where anti-PD-1 agents are restricted to advanced/metastatic disease, against one where anti-PD-1 agents are used as adjuvant or perioperative therapy for early-stage cancers. Population and incidence inputs were obtained from nation-specific sources, while efficacy and quality of life data were informed by the individual trials. Productivity outcomes were estimated using a human capital approach. Results: Between 2023 and 2032, 57,075 (60.4%) of 94,426 patients with early-stage cancers eligible for adjuvant or perioperative treatment are estimated to receive anti-PD-1 agents. This was associated with an increase in total life years (8,878, 2.4%), quality-adjusted life-years (9,029, 3.1%), and event-/disease-/recurrence-free life years (25,149, 9.0%), and a reduction in progression events or recurrences (6,839, 16.8%), active metastatic treatments (4,845, 14.0%), and deaths (3,013, 16.2%). The clinical benefits also resulted in a gain in productive years (20,717, 17.6%). Conclusion: The use of anti-PD-1 agents in adjuvant or perioperative settings can lead to substantial health and productivity gains. Effective planning and investment are needed for timely access to these agents for patients.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".