Assessing the impact of increasing lung screening eligibility by relaxing the maximum years‐since‐quit threshold: A simulation modeling study
Bibliographic record
Abstract
BACKGROUND: In 2021, the US Preventive Services Task Force expanded its lung screening recommendation to include persons aged 50-80 years who had ever smoked and had at least 20 pack-years of exposure and less than 15 years since quitting (YSQ). However, studies have suggested that screening persons who formerly smoked with longer YSQ could be beneficial. METHODS: The authors used two validated lung cancer models to assess the benefits and harms of screening using various YSQ thresholds (10, 15, 20, 25, 30, and no YSQ) and the age at which screening was stopped. The impact of enforcing the YSQ criterion only at entry, but not at exit, also was evaluated. Outcomes included the number of screens, the percentage ever screened, screening benefits (lung cancer deaths averted, life-years gained), and harms (false-positive tests, overdiagnosed cases, radiation-induced lung cancer deaths). Sensitivity analyses were conducted to evaluate the effect of restricting screening to those who had at least 5 years of life expectancy. RESULTS: As the YSQ criterion was relaxed, the number of screens and the benefits and harms of screening increased. Raising the age at which to stop screening age resulted in additional benefits but with more overdiagnosis, as expected, because screening among those older than 80 years increased. Limiting screening to those who had at least 5 years of life expectancy would maintain most of the benefits while considerably reducing the harms. CONCLUSIONS: Expanding screening to persons who formerly smoked and have greater than 15 YSQ would result in considerable increases in deaths averted and life-years gained. Although additional harms would occur, these could be moderated by ensuring that screening is restricted to only those with reasonable life expectancy.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".