Impact of pembrolizumab on health outcomes and productivity in earlier-stage cancers within the US Medicare population
Bibliographic record
Abstract
AIM: Pembrolizumab has demonstrated significant improvements in clinical outcomes across multiple cancers. This study quantifies health benefits and productivity gains of pembrolizumab as a perioperative/adjuvant treatment in five earlier-stage cancer indications (melanoma stage IIB-C and III, triple-negative breast cancer, renal cell carcinoma, resectable and resected non-small cell lung cancer) within the US Medicare beneficiary population. MATERIALS AND METHODS: A population-level, multi-indication decision model was developed to project the health and productivity gains of pembrolizumab. Over the 10 year period (2025-2034), a new patient cohort enters the model each year, and every cohort is followed through to 2034. Two scenarios were compared for cohorts of Medicare beneficiaries newly eligible for perioperative/adjuvant pembrolizumab: (1) a world without pembrolizumab, using only historical perioperative/adjuvant treatment options, and (2) a world where pembrolizumab is added as perioperative/adjuvant therapy for FDA-approved cancers. Outcomes included life years (LYs), quality-adjusted LYs (QALYs), number of events or recurrences, number of systemic treatments for metastatic disease, deaths, and caregivers' productive years lost. Inputs include clinical trial efficacy, Medicare and US population data, epidemiology, and market share information. RESULTS: Adding perioperative/adjuvant pembrolizumab is projected to increase recurrence-free LYs by 122,918 (+10%), total LYs by 56,704 (+4%), and QALYs by 55,483 (+4%). This strategy could reduce the number of recurrences by 35,558 (-16%), number of systemic treatments for metastatic disease by 32,962 (-16%), deaths following the first recurrence by 8,962 (-10%), and total deaths by 16,146 (-11%). Caregivers' productive years lost are estimated to decline by 9,392 (-18%) through reduced absenteeism and presenteeism. CONCLUSIONS: Perioperative/adjuvant pembrolizumab in earlier-stage cancers can meaningfully improve clinical outcomes for Medicare patients and reduce caregiver productivity losses. Sustained investment and access to pembrolizumab are critical to fully realize these benefits for patients and society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".