Personalizing lung cancer screening recommendations for heterogeneous populations: a microsimulation study
Bibliographic record
Abstract
BACKGROUND: There is little guidance on how to personalize recommendations for lung cancer screening that accounts for the variation in expected net benefit from screening. We sought to explore the individual and population implications of identifying net benefit thresholds where lung cancer screening could be encouraged, discouraged, or offered as an option through neutral shared decision making due to screening being highly preference sensitive. METHODS: With a simulated US population of 40- to 80-year-old individuals who had ever smoked, we used microsimulation to estimate individualized quality-adjusted life-years saved with lung cancer screening. We then identified 2 net benefit thresholds for lung cancer screening that account for a range of patient preferences and scientific uncertainties and compared this approach with the current United States Preventive Services Task Force (USPSTF) guidelines. RESULTS: Our simulated population included 59 million people. In total, 15 million were USPSTF eligible; of those, 53% (8 million) maintained net benefit, even after accounting for unfavorable preferences about screening. Of the USPSTF population, 3% (450 000) are considered low net benefit (routinely discourage) and 47% (7 million) are in an intermediate gray area, where net benefit depends on patient preferences about lung cancer screening (offer-neutral shared decision making). Among adults who had ever smoked, 2.5 million are high net benefit but excluded by current USPSTF criteria; 20.5 million US adults who had ever smoked are intermediate net benefit but currently excluded by USPSTF criteria. CONCLUSIONS: We estimate that half of the USPSTF lung cancer screening-eligible population is in a high-net-benefit group where lung cancer screening could be routinely encouraged. Current lung cancer screening eligibility criteria likely exclude many ever-smokers who are high net benefit and many more with intermediate net benefit.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".