Could chest X-ray screening for lung cancer be cost-effective?
Bibliographic record
Abstract
BACKGROUND: Currently, no screening program for lung cancer is advocated, yet recent review of the clinical trials has raised questions about the conclusion that it would not be effective. If a screening program is to be considered, its potential economic impact needs to be assessed. METHODS: An economic model was created comparing lung cancer mortality in male smokers ages 45-80 years, screened versus unscreened. Estimates of the potential reduction in mortality and cost of screening are applied. The outcomes of the model include deaths avoided, life years gained, net costs, and cost-effectiveness. RESULTS: The base analysis (mortality reduction of 18%) estimates that nearly 3000 deaths would be avoided in a population of 100,000 male smokers age 40-80 years, at a cost-effectiveness of $9000 per undiscounted life year gained. A program resulting in only 6% mortality reduction would increase the ratio to $25,000 per undiscounted life years gained. CONCLUSIONS: If further examination of lung cancer screening supports its effectiveness, the results of this model suggest that implementation would be economically efficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".