Eligibility and Prognostic Performance of Smoking Duration–Based Versus Pack-Year–Based U.S. National Lung Cancer Screening Criteria Across Racial and Ethnic Groups
Bibliographic record
Abstract
BACKGROUND: The U.S. Preventive Services Task Force expanded lung cancer (LC) screening eligibility in 2021 (USPSTF-2021) by decreasing the minimum number of smoking pack-years from 30 to 20. Underrepresented minorities still experience disparities in screening eligibility. OBJECTIVE: ) model (secondary outcome) across diverse racial and ethnic groups. DESIGN: Prospective, population-based Multiethnic Cohort linked to SEER (Surveillance, Epidemiology, and End Results) registries. SETTING: California and Hawai'i, with recruitment from 1993 to 1996. PARTICIPANTS: 105 261 adults aged 45 to 75 years with a history of smoking. MEASUREMENTS: Hypothetical eligibility and prognostic performance (sensitivity and specificity) in detecting 6-year LC. RESULTS: 6-year threshold of 1.1% improved both sensitivity and specificity in the overall cohort. However, it widened the eligibility gap between Latinos and Whites (14.4% vs. 31.3%) and demonstrated lower sensitivity in Latinos than duration-based criteria (59.7% vs. 69.8%). LIMITATIONS: Cohort geography and enrollment period may limit generalizability. Overdiagnosis was not measured. CONCLUSION: Compared with USPSTF-2021, the 30-year duration-based criteria could reduce the eligibility gaps among African Americans and Latinos relative to Whites while improving 6-year LC detection sensitivity across all races. PRIMARY FUNDING SOURCE: National Institutes of Health.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".