Quality requirements to establish a successful lung cancer screening program
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related deaths globally.There is a strong body of evidence from the last two decades to support the effectiveness of lung cancer screening (LCS) with low radiation dose computed tomography (LDCT) in reducing lung cancer mortality and all-cause mortality.National programmes are approved and either ongoing or in planning in Poland, Croatia, the UK, Canada, Australia and the US.Other countries are proceeding with pilot programmes, some of which are framed as research studies.The European Commission's Group of Chief Scientific Advisors recommended that lung cancer screening (LCS) be added to the other established cancer screening programs in Europe and the European Council recommended that LCS be implemented in a stepwise approach depending on national priorities.However, it is essential that clinical and cost-effectiveness shown in studies and pilot programmes are replicated in national programmes.This is achieved through the use of evidence-based strategies across each element of screening from participant selection to treatment.This review addresses key quality requirements identified in the literature which must form part of a successful LCS program.We describe the challenges and, where possible, suggest solutions for the implementation of screening.We will highlight the areas for further research including risk-prediction models for screening eligibility, optimising recruitment methods, personalisation of screening intervals, biomarkers, smoking cessation integration, and artificial intelligence.
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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 | 0.382 | 0.563 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".