Lung cancer screening: enhancing risk stratification and minimising harms by incorporating information from screening results
Bibliographic record
Abstract
Two large randomised controlled trials of screening for lung cancer with low-dose CT (LDCT)—the National Lung Screening Trial (NLST) and the Dutch-Belgian lung cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trial)—have both shown substantial lung cancer mortality reduction in the LDCT arm.1 Although there is now strong evidence that screening for lung cancer with LDCT reduces lung cancer mortality, concerns have been raised about the potential costs and harms of implementing annual lung cancer screening programmes. Annual screening with LDCT has been recommended in the USA, based on the evidence provided by the NLST design and modelling extrapolations.2 3 However, uptake of screening has been poor, with the latest data showing only 3.9% of those eligible have actually been screened.4 A cost-effectiveness analysis for Ontario, Canada, showed that annual screening scenarios were more cost-effective than biennial screening,5 but there is ongoing debate about whether all of those eligible for lung cancer screening actually require an annual LDCT. NELSON was the only trial to compare the effects of different screening intervals between rounds. While the proportion of advanced stage cancers increased somewhat, a 2-year interval still had a good performance compared with a 1-year interval. However, there was an increase in interval cancers and a higher proportion of more advanced stage cancers for a 2.5-year interval compared with a 2-year interval suggesting that this may be too long.6 Risk prediction models have been suggested to select eligible participants at high risk of lung cancer and have been shown to be superior to selection using age and smoking status alone.7 8 Incorporating information from LDCT results may allow them to aid in the personalisation of the screening regimen. In NELSON, participants with negative LDCT results were less likely to have lung cancer detected at a …
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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 | 0.041 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".