Disparity in Lung Cancer Screening Among Smokers and Nonsmokers in China: Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality in smokers; however, the evidence in nonsmokers is scarce. OBJECTIVE: This study aimed to evaluate the participant rate and effectiveness of one-off LDCT screening for lung cancer among smokers and nonsmokers. METHODS: A population-based prospective cohort study was performed to enroll participants aged between 40 and 74 years from 2013 to 2019 from 4 cities in Zhejiang Province, China. Participants who were evaluated as having a high risk of lung cancer from an established risk score model were recommended to undergo LDCT screening. Follow-up outcomes were retrieved on June 30, 2020. The uptake rate of LDCT screening for evaluated high-risk participants and the detection rate of early-stage lung cancer (stage 0-I) were calculated. The lung cancer incidence, lung cancer mortality, and all-cause mortality were compared between the screened and nonscreened groups. RESULTS: At baseline, 62.56% (18,818/30,079) of smokers and 6% (5483/91,455) of nonsmokers were identified as high risk (P<.001), of whom 41.9% (7885/18,818) and 66.31% (3636/5483) underwent LDCT screening (P<.001), respectively. After a median follow-up of 5.1 years, 1100 lung cancer cases and 456 all-cause death cases (116 lung cancer death cases) were traced. The proportion of early-stage lung cancer among smokers was 60.3% (173/287), which was lower than the proportion of 80.3% (476/593) among nonsmokers (P<.001). Among smokers, a higher proportion was found in the screened group (72/106, 67.9%) than the nonscreened group (56/114, 49.1%; P=.005), whereas no significance was found (42/44, 96% vs 10/12, 83%; P=.20) among nonsmokers. Compared with participants who were not screened, LDCT screening in smokers significantly increased lung cancer incidence (hazard ratio [HR] 1.39, 95% CI 1.09-1.76; P=.007) but reduced lung cancer mortality (HR 0.52, 95% CI 0.28-0.96; P=.04) and all-cause mortality (HR 0.47, 95% CI 0.32-0.69; P<.001). Among nonsmokers, no significant results were found for lung cancer incidence (P=.06), all-cause mortality (P=.89), and lung cancer mortality (P=.17). CONCLUSIONS: LDCT screening effectively reduces lung cancer and all-cause mortality among high-risk smokers. Further efforts to define high-risk populations and explore adequate lung cancer screening modalities for nonsmokers are needed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".