A Systematic Review of Cost-Effectiveness Studies on Pancreatic Cancer Screening
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer (PC) is among the deadliest types of cancer globally. While early detection helps avert adverse outcomes, screening is only recommended for individuals at high risk, specifically those with familial and/or genetic predispositions. The objectives of this study are to systematically review primary studies on the cost-effectiveness of PC screening and to identify the critical factors that influence cost-effectiveness. METHODS: This systematic review was performed using PRISMA guidelines. Economic evaluation studies on PC screening were identified from searches on the SCOPUS and PubMed databases. The quality of reporting of the selected articles was assessed according to CHEERS 2022. Using predefined inclusion and exclusion criteria, two reviewers conducted the title-abstract review, full-text review, and data extraction to select relevant articles. The authors' consensus was used to settle disagreements. The primary outcome was the incremental cost-effectiveness ratio, measured by cost per quality-adjusted life year and cost per life year saved. RESULTS: Nine studies were selected for the final review. Most studies demonstrated that one-time screening for PC among high-risk individuals was cost-effective compared with no screening, while others found annual screening to also be cost-effective. High-risk was generally defined as having a >5% lifetime risk of PC and included individuals with either familial pancreatic cancer (FPC) or genetic susceptibility syndromes such as Peutz-Jeghers Syndrome, hereditary pancreatitis, hereditary non-polypoid colorectal cancer syndrome, familial adenomatous polyposis, and BRCA2 mutations. Individuals with new-onset diabetes (NOD) were also considered high-risk. Screening using mainly endoscopic ultrasound was cost-effective among FPC individuals and those with genetic syndromes. Risk-based screening was also cost-effective among patients with NOD. CONCLUSION: Screening for PC is cost-effective among selected high-risk individuals. However, cost-effectiveness depends on epidemiological factors, cost, the diagnostic performance of screening tools, and the overall design of studies.
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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 | 0.023 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".