Should we screen for lung cancer? A 10-country analysis identifying key decision-making factors
Bibliographic record
Abstract
The need for early detection, both early diagnosis and screening is essential for improved prognosis in lung cancer. The effectiveness of lung cancer screening using low-dose computed tomography (LDCT) for high-risk patients has been shown by extensive clinical evidence including the National Lung Cancer Screening Trial (NLST) and the Dutch-Belgian lung cancer screening trial (NELSON) which has triggered political consideration of a formal programme across countries. However, implementation of these is still limited. This study investigates how governments make decisions on the implementation of lung cancer screening, identifying key consideration factors through 10 case study countries: Australia, Canada, Croatia, France, Germany, Japan, South Korea, Switzerland, UK, and US. We identified five decision-making factors (1) recognition of the disease burden and the value of early detection, (2) strong clinical data showing mortality reduction and benefit-risk analysis relevant to the local context, (3) cost-effectiveness data and budget impact, (4) local feasibility demonstration and (5) a clear and integrated decision-making mechanism involving relevant stakeholders. The set of factors identified in this paper can help advocates address knowledge gaps, identify the key focus areas for discussions with policymakers evaluating the opportunities for lung cancer screening programmes in their local context. Ultimately, this should allow policymakers to make more informed decisions on lung cancer screening to best improve lung cancer outcomes.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".