ArticleThorax2022
Selection of eligible participants for screening for lung cancer using primary care data.
Article in Thorax, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- A systematic review and meta-analysis of lung cancer risk prediction models.Acta oncologica (Stockholm, Sweden) · 2025Pooled it
- Article
- Beyond nodules: body composition as a biomarker for future lung cancer.European radiology · 2025Article
- Cataloguing and evaluating Irish health data resources for primary care cancer research.BJC reports · 2025Review
- Head-to-head comparisons of risk discrimination by questionnaire-based lung cancer risk prediction models: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Optimal Pathways to Lung Cancer Screening in Primary Care Settings: A Scoping Review.Current oncology (Toronto, Ont.) · 2024Article
- Efficacy of Digital Outreach Strategies for Collecting Smoking Data: Pragmatic Randomized Trial.JMIR formative research · 2024Article
- Article
- Assessing, updating and utilising primary care smoking records for lung cancer screening.BMC pulmonary medicine · 2023Article
- Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study.PLoS medicine · 2023Article
- ERS International Congress 2022: highlights from the Thoracic Oncology Assembly.ERJ open research · 2023Article
- The value of blood-based measures of liver function and urate in lung cancer risk prediction: A cohort study and health economic analysis.Cancer epidemiology · 2023Article
- Utilisation of primary care electronic patient records for identification and targeted invitation of individuals to a lung cancer screening programme.Lung cancer (Amsterdam, Netherlands) · 2022Article
- A Feasible Path to Reductions in Racial and Ethnic Disparities in Lung Cancer Screening?JNCI cancer spectrum · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 9 institutions in 5 countries.
Funding
Abstract
Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown.
methodThe Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLP
resultsOver 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLP
conclusionRisk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data.
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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.