Treatment capacity required for full‐scale implementation of lung cancer screening in the United States
Bibliographic record
Abstract
BACKGROUND: Full-scale implementation of lung cancer screening in the United States will increase detection of early stages. This study was aimed at assessing the capacity required for treating those cancers. METHODS: A well-established microsimulation model was extended with treatment data from the National Cancer Database. We assessed how treatment demand would change when implementing lung cancer screening in 2018. Three policies were assessed: 1) annual screening of current smokers and former smokers who quit fewer than 15 years ago, aged 55 to 80 years, with a smoking history of at least 30 pack-years (US Preventive Services Task Force [USPSTF] recommendations); 2) annual screening of current smokers and former smokers who quit fewer than 15 years ago, aged 55 to 77 years, with a smoking history of at least 30 pack-years (Centers for Medicare and Medicaid Services [CMS] recommendations); and 3) annual screening of current smokers and former smokers who quit fewer than 10 years ago, aged 55 to 75 years, with a smoking history of at least 40 pack-years (the most cost-effective policy in Ontario [Ontario]). The base-case screening adherence was a constant 50%. Sensitivity analyses assessed other adherence levels, including a linear buildup to 50% between 2018 and 2027. RESULTS: The USPSTF policy would require 37.0% more lung cancer surgeries in 2015-2040 than no screening, 2.2% less radiotherapy, and 5.4% less chemotherapy; 5.7% more patients would require any therapy. The increase in surgical demand would be 96.1% in 2018, 46.0% in 2023, 38.3% in 2028, and 24.9% in 2040. Adherence strongly influenced results. By 2018, surgical demand would range from 52,619 (20% adherence) to 96,121 (80%). With a gradual buildup of adherence, the increase in surgical demand would be 9.6% in 2018, 38.3% in 2023, 42.0% in 2028, and 24.4% in 2040. Results for the CMS and Ontario policies were similar, although the changes in comparison with no screening were smaller. CONCLUSIONS: Full-scale implementation of lung cancer screening causes a major increase in surgical demand, with a peak within the first 5 years. A gradual buildup of adherence can spread this peak over time. Careful surgical capacity planning is essential for successfully implementing screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".