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Sökning: WFRF:(Pisu Francesco)

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1.
  • Pertl, Leonhard, et al. (författare)
  • Exports and Productivity : Comparable Evidence for 14 Countries
  • 2007
  • Ingår i: Policy Research Working Papers. - New York : World Bank. - 1813-9450.
  • Rapport (refereegranskat)abstract
    • The authors use comparable micro level panel data for 14 countries and a set of identically specified empirical models to investigate the relationship between exports and productivity. The overall results are in line with the big picture that is by now familiar from the literature: Exporters are more productive than non-exporters when observed and unobserved heterogeneity are controlled for, and these exporter productivity premia tend to increase with the share of exports in total sales; there is strong evidence in favour of self-selection of more productive firms into export markets, but nearly no evidence in favour of the learning-by-exporting hypothesis. The authors document that the exporter premia differ considerably across countries in identically specified empirical models. In a meta-analysis of their results the authors find that countries that are more open and have more effective government report higher productivity premia. However, the level of development per se does not appear to be an explanation for the observed cross-country differences.
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2.
  • Pisu, Francesco, et al. (författare)
  • Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography
  • 2024
  • Ingår i: European Radiology. - : Springer. - 0938-7994 .- 1432-1084. ; 34:6, s. 3612-3623
  • Tidskriftsartikel (refereegranskat)abstract
    • Objectives: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)–based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques.Material and methods: We conducted a multicenter, retrospective diagnostic study (March 2013–May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications.Results: We included 790 patients (median age 72, IQR [61–80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63–76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58–0.78; p <.001) and sensitivity 80% (79–81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients.Conclusion: The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy.Clinical relevance: The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients.Key Points: • While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient’s arrival at the hospital, which streamlines the diagnosis of symptoms using ML. Graphical Abstract: [Figure not available: see fulltext.].
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