IntroductionAlthough lung cancer screening is being implemented in the UK, there is uncertainty about the optimal invitation strategy. Here, we report participation in a community screening programme following a population-based invitation approach, examine factors associated with participation, and compare outcomes with hypothetical targeted invitations.MethodsLetters were sent to all individuals (age 55–80) registered with a general practice (n=35 practices) in North and East Manchester, inviting ever-smokers to attend a Lung Health Check (LHC). Attendees at higher risk (PLCOm2012NoRacescore≥1.5%) were offered two rounds of annual low-dose CT screening. Primary care recorded smoking codes (live and historical) were used to model hypothetical targeted invitation approaches for comparison.ResultsLetters were sent to 35 899 individuals, 71% from the most socioeconomically deprived quintile. Estimated response rate in ever-smokers was 49%; a lower response rate was associated with younger age, male sex, and primary care recorded current smoking status (adjOR 0.55 (95% CI 0.52 to 0.58), p<0.001). 83% of eligible respondents attended an LHC (n=8887/10 708). 51% were eligible for screening (n=4540/8887) of whom 98% had a baseline scan (n=4468/4540). Screening adherence was 83% (n=3488/4199) and lung cancer detection 3.2% (n=144) over 2 rounds. Modelled targeted approaches required 32%–48% fewer invitations, identified 94.6%–99.3% individuals eligible for screening, and included 97.1%–98.6% of screen-detected lung cancers.DiscussionUsing a population-based invitation strategy, in an area of high socioeconomic deprivation, is effective and may increase screening accessibility. Due to limitations in primary care records, targeted approaches should incorporate historical smoking codes and individuals with absent smoking records.

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We propose a model that represents the dynamic behaviour of a monetary union comprising two countries whose natural interest rates are initially unequal. This initial disparity and the subsequent application of a common monetary policy generate different national inflation rates and lead to losses of competitiveness, foreign deficits, and the indebtedness of one country with respect to the other. We propose as a viability criterion for the modelled monetary union a combination of non‐explosive foreign debt and the ability of the central bank to neutralize the contracting effects of taking on additional debt to avoid falling into a liquidity trap.

Few-Shot Class Incremental Learning (FSCIL) is a challenging continual learning task, where limited training examples are available during several learning sessions. To succeed in this task, it is necessary to avoid over-fitting new classes caused by biased distributions in the few-shot training sets. The general approach to address this issue involves enhancing the representational capability of a predefined backbone architecture by adding special modules for backward compatibility with older classes. However, this approach has not yet solved the dilemma of ensuring high classification accuracy over time while reducing the gap between the performance obtained on larger training sets and the smaller ones. In this work, we propose an alternative approach called Continual Parameter-Efficient CLIP (CPE-CLIP) to reduce the loss of information between different learning sessions. Instead of adapting additional modules to address information loss, we leverage the vast knowledge acquired by CLIP in large-scale pre-training and its effectiveness in generalizing to new concepts. Our approach is multimodal and parameter-efficient, relying on learnable prompts for both the language and vision encoders to enable transfer learning across sessions. We also introduce prompt regularization to improve performance and prevent forgetting. Our experimental results demonstrate that CPE-CLIP significantly improves FSCIL performance compared to state-of-the-art proposals while also drastically reducing the number of learnable parameters and training costs.Recent research has focused on solving these problems through various approaches, such as meta-learning [57, 34], regularization techniques [30], or knowledge distillation [38,6,62]. These methods have shown promising results in achieving incremental learning over time with a limited amount of data available. The general approaches consist in enhancing the basic representational capability of a predefined backbone architecture by adding special modules to entail backward compatibility with older classes during learning sessions. These solutions are computationally expensive since they need a large number of iterations in each session to adapt the additional modules to new classes while maintaining backward compatibility. Despite the high computational cost, they still fail to efficiently reduce the gap between the performance obtained on larger training sets