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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".