Effectiveness of interventions for increasing lung cancer screening uptake: A systematic review and meta-analysis of randomized clinical trials
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the effectiveness of interventions for increasing lung cancer screening (LCS) uptake and to identify factors influencing their implementation. METHODS: We searched MEDLINE, CINAHL, EMBASE, Cochrane Library, and Web of Science from January 2010 to November 2025. Included studies were randomized controlled trials involving high-risk adults eligible for LCS with low-dose computed tomography, evaluating interventions to improve screening uptake as a primary or secondary outcome. RESULTS: Eleven trials were included, primarily from the United States (N = 9). Interventions were categorized as patient navigation, decision aids, educational video/film, targeted invitation/outreach, and multi-component intervention. Overall, interventions showed a modest but significant effect on LCS uptake (RR = 1.34; 95 % CI: 1.02, 1.76). The multi-component intervention (RR = 2.11; 95 % CI: 1.21, 3.68) demonstrated significant effects, while patient navigation showed potential (RR = 2.18; 95 % CI: 0.53, 9.08). Innovation and inner setting were identified as potentially important factors influencing intervention implementation. CONCLUSIONS: Interventions modestly increased LCS uptake, with multi-component intervention and patient navigation showing the most promising effects. Future research should prioritize multicomponent strategies that address the entire screening continuum, equity-focused designs for priority populations, and trials in diverse international settings.
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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 | 0.034 | 0.088 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.032 | 0.052 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".