SynthesisPublic health reviews2026
Use and impact of risk-based eligibility models in low-dose computed tomography lung cancer screening: a systematic review.
Synthesis in Public health reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Use and impact of risk-based eligibility models in low-dose computed tomography lung cancer screening: a systematic review.Public health reviews · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Low-dose computed tomography lung cancer screening (LDCT-LCS) significantly reduces mortality, yet identifying high-risk individuals while reducing over-screening remains challenging. Risk-based eligibility models are promising to optimize participant selection. Within the European context, we assessed the types, outcomes, and impact of these risk-based eligibility models for LDCT-LCS. Methods: We systematically reviewed prediction model studies (PROSPERO CRD42025648906) across EMBASE, MEDLINE, and Cochrane Central Register of Controlled Trials. We included original research on adults aged 18+ at risk for LC, excluding East-Asian populations. Study characteristics, model type, performance and outcomes were extracted for narrative synthesis. Results: The review included 46 articles (2003-2025), identifying 39 risk-prediction models. Models were primarily statistical (72%); PLCOm2012 was most frequent. Age (100%), smoking duration (91%), and intensity (72%) were the most common variables. Risk models improved eligibility and demonstrated cost-effectiveness over traditional criteria, though heterogeneity and population-specific calibration remain challenges. Conclusion: Risk-based eligibility models improve LDCT-LCS efficiency by enhancing detection rates and personalization. While PLCOm2012 is prominent, addressing model heterogeneity, ensuring population-specific validation, and calibration are crucial to optimize LDCT-LCS outcomes in Europe. Systematic Review Registration: Identifier CRD42025648906.
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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.