Publications by authors named "Pietrafesa E"

Article Synopsis
  • Obstructive sleep apnoea (OSA) is a common sleep disorder linked to significant health risks and economic costs, and machine learning (ML) may help predict its severity and understand its causes.
  • This study involved adult patients suspected of OSA who underwent assessments and sleep monitoring, using various ML models to analyze their clinical data.
  • Results showed different ML models had varying levels of accuracy in predicting OSA severity, with certain demographic and health factors, like body measurements and hypertension, being important predictors.
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Introduction: Obstructive Sleep Apnea Syndrome (OSAS) is a relevant public health problem; dentists can play an important role in screening patients with sleep disorders by using validated tools and referring patients to a specialist, thereby promoting an interdisciplinary approach. The aim of the study is to identify if the OSAS severity, measured by the apnea-hypopnea index (AHI), and some anthropometric measurements are associated with the Friedman Tongue Position (FTP) within a population with dysmetabolic comorbidities.

Materials And Methods: A questionnaire containing information about clinical data including height, weight, Body Mass Index (BMI), neck circumference, waist circumference, hip circumference and FTP was administered.

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Obstructive sleep apnea syndrome (OSAS) is an under-recognized clinical condition and is correlated with sleepiness and impaired cognitive function. Objectives: The primary aim of this systematic review, developed within the Sleep@OSA project, was to determine the correlations of obstructive sleep apnea syndrome, daytime sleepiness and sleep-disordered breathing with the risk of car accidents in adult working populations; a secondary aim was to analyze the epidemiologic data with a gender-based approach to identify differences between women and men in the data and in associated risk factors. Methods: Clinical trials and studies reporting data on the frequency of car accidents involving adult working population with daytime sleepiness and/or OSAS compared with a control group of participants were included.

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Many workers are exposed to the effects of heat and often to extreme temperatures. Heat stress has been further aggravated during the COVID-19 pandemic by the use of personal protective equipment to prevent SARS-CoV-2 infection. However, workers' risk perception of heat stress is often low, with negative effects on their health and productivity.

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Introduction: OSAS is an emerging public health problem. Early diagnosis in adults with comorbidities is the gold standard to avoid complications caused by a late diagnosis. The aim of the study, part of the SLeeP@SA project, was to identify within a population with dysmetabolic comorbidities the association of occlusal clinical signs, defined by orthodontic parameters, and of the anthropometric phenotype, with the severity of OSAS.

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The pandemic has been afflicting the planet for over a year and from the occupational point of view, healthcare workers have recorded a substantial increase in working hours. The use of personal protective equipment (PPE), necessary to keep safe from COVID-19 increases the chances of overheating, especially during the summer seasons which, due to climate change, are becoming increasingly warm and prolonged. A web survey was carried out in Italy within the WORKLIMATE project during the summer and early autumn 2020.

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Toxoplasmosis is a worldwide zoonotic infectious disease caused by Toxoplasma gondii. This infection is estimated to affect about a third of the world's population. The aim of this study was to evaluate the knowledge of Italian women about toxoplasmosis and its forms of transmission, clinical manifestations, diagnosis and prevention through two different modalities (e-research and traditional research).

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