ArticleTrials2022
Study protocol of a multiphase optimization strategy trial (MOST) for delivery of smoking cessation treatment in lung cancer screening settings.
Article in Trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03315910 (Implementing Tobacco Treatment in Low Dose CT Lung Cancer Screening Sites), which is not on this map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Implementing Tobacco Treatment in Low Dose CT Lung Cancer Screening Sites
Who cites it
5 citing papers in PubMed.
- Cost-Effectiveness of Smoking Cessation Interventions Integrated Into Lung Cancer Screening.JAMA network open · 2026Article
- Power Calculation in 2Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Patient and Healthcare Provider Barriers in the LDCT Lung Cancer Screening Continuum.Diagnostics (Basel, Switzerland) · 2026Review
- Utilizing a patient advocacy-led clinical network to engage diverse, community-based sites in implementation-effectiveness research.BMC health services research · 2024Article
- Factors Associated With Receipt of Tobacco Treatment Integrated With Nurse Navigation in a Centralized Lung Cancer Screening Program at an Urban Academic Medical Center.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
backgroundThere is widespread agreement that the integration of cessation services in lung cancer screening (LCS) is essential for achieving the full benefits of LCS with low-dose computed tomography (LDCT). There is a formidable knowledge gap about how to best design feasible, effective, and scalable cessation services in LCS facilities. A collective of NCI-funded clinical trials addressing this gap is the Smoking Cessation at Lung Examination (SCALE) Collaboration.
methodsThe Cessation and Screening to Save Lives (CASTL) trial seeks to advance knowledge about the reach, effectiveness, and implementation of tobacco treatment in lung cancer screening. We describe the rationale, design, evaluation plan, and interventions tested in this multiphase optimization strategy trial (MOST). A total of 1152 screening-eligible current smokers are being recruited from 18 LCS sites (n = 64/site) in both academic and community settings across the USA. Participants receive enhanced standard care (cessation advice and referral to the national Quitline) and are randomized to receive additional tobacco treatment components (motivational counseling, nicotine replacement patches/lozenges, message framing). The primary outcome is biochemically validated, abstinence at 6 months follow-up. Secondary outcomes are self-reported smoking abstinence, quit attempts, and smoking reduction at 3 and 6 months. Guided by the Implementation Outcomes Framework (IOF), our evaluation includes measurement of implementation processes (reach, fidelity, acceptability, appropriateness, sustainability, and cost).
conclusionWe will identify effective treatment components for delivery by LCS sites. The findings will guide the assembly of an optimized smoking cessation package that achieves superior cessation outcomes. Future trials can examine the strategies for wider implementation of tobacco treatment in LDCT-LCS sites.
trial registrationClinicalTrials.gov NCT03315910.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.