The impact on productivity costs of reducing unemployment in patients with advanced breast cancer: A model estimation based on a Portuguese nationwide observational study.

This study aimed to characterize the employment status and work-related conditions of patients with advanced breast cancer (ABC) in Portugal and quantify the productivity costs of premature abandonment of the work market while evaluating strategies to promote employment. The analysis was based on a cumulative incidence model for estimation of ABC prevalence and on a cross-sectional study characterizing the employment status of patients with ABC. This study was conducted in Portuguese hospitals, between Nov2021-Dec2022 and included patients diagnosed with ABC for at least 6 months, aged 66 or less and consenting for a self-answered questionnaire regarding work status.

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Purpose: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.

Methods: We included clinical and ultrasonographic attributes from 1526 breast lesions classified as BI-RADS 3, 4a, 4b, 4c, 5, and 6 that underwent US-guided breast biopsy in four institutions. We selected the most informative attributes to train nine machine learning models, ensemble models and models with tuned threshold to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset).

Effect of pre-cooling on the shelf-life and quality of formosa papaya.

Papaya is a climacteric fruit, rapidly ripening after harvesting due to ethylene production and increased respiratory rate. This swift ripening results in softening of fruit tissues, shortening the fruit shelf life. Pre-cooling serves as an alternative to minimize fruit ripening and post-harvest losses by reducing metabolism.

Economic evaluations of medical devices in paediatrics: a systematic review and a quality appraisal of the literature.

Background: Although economic evaluations (EEs) have been increasingly applied to medical devices, little discussion has been conducted on how the different health realities of specific populations may impact the application of methods and the ensuing results. This is particularly relevant for pediatric populations, as most EEs on devices are conducted in adults, with specific aspects related to the uniqueness of child health often being overlooked. This study provides a review of the published EEs on devices used in paediatrics, assessing the quality of reporting, and summarising methodological challenges.