Two-Year Follow-Up of a Randomized Controlled Study of Integrated Smoking Cessation in a Lung Cancer Screening Program
Bibliographic record
Abstract
Introduction Smoking cessation activities incorporated into lung cancer screening programs have been broadly recommended, but studies to date have not exhibited increased quit rates associated with cessation programs in this setting. We aimed to determine the long-term effectiveness of smoking cessation counseling in smokers presenting for lung cancer screening. Methods This was a randomized control trial of an intensive, telephone-based smoking cessation counseling intervention incorporating lung cancer screening results versus usual care (information pamphlet). This analysis reports on the long-term impact (24-mo) of the intervention on abstinence from smoking. Results A total of 337 active smokers who participated in the screening study were randomized to active smoking cessation counseling (n = 171) or control arm (n = 174) and completed a 24-month assessment. The 30-day smoking abstinence rates at 24 months postrandomization was 18.3% and 21.4% in the control and intervention arms, respectively—a 3.1% difference (95% confidence interval: −5.4 to 11.6, p = 0.48). No statistically significant differences in the 7-day abstinence, the use of pharmacologic cessation aids, nicotine replacement therapies, nor intent to quit in the following 30 days were noted ( p > 0.05). The abstinence rates at 24-months were higher overall than at 12-months (19.9% versus 13.3%, p < 0.001), and smoking intensity was lower than at baseline for ongoing smokers. Conclusions A telephone-based smoking cessation counseling intervention incorporating lung cancer screening results did not result in increased long-term cessation rates versus written information alone in unselected smokers undergoing lung cancer screening. Overall, quit rates were high and continued to improve throughout participation in the screening program. (ClinicalTrials.gov NCT02431962).
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".