An epidemiologic survey of celiac disease in the Terni area (Umbria, Italy) in 2002-2010

The present work is an epidemiology survey of celiac disease in the province of Terni (Umbria, Italy) in 2002?2010. Data were collected from the Local Health Unit (LHU) 4 (ASL 4), Terni database and were extrapolated from the overall population of 232,540 (as of 2010) by identifying residents with prescription charge exemptions for celiac disease-oriented drugs. Prevalence and incidence analysis over the timeframe being examined showed that prevalence (330 cases in 2010) has consistently been increasing from 2002 to 2010, whereas incidence has remained essentially the same with minor, yearly fluctuations. Both prevalence and incidence were higher in females than in males. Most patients were diagnosed as young adults, with the highest rates in the 10-14, 35-40 and 55-60 age groups. Thus, in the area of investigation, there is evidence for consistent delayed diagnosis, raising the possibility that the atypical form the disease, more difficult to recognize and more likely to escape early diagnosis, may have become increasingly commoner over time. Because the current prevalence of the disease in the Terni area is estimated to approximate 1%, the anticipated number of cases should amount to 2,325, which value contrasts with the currently reported 330 diagnoses. It is suggested that the current illnessdefining criteria should be revised so to implement early diagnosis and improve the patients? quality of life and access to treatment

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized

Effectiveness of Platform-Based Robot-Assisted Rehabilitation for Musculoskeletal or Neurologic Injuries: A Systematic Review

During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson’s disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices

Approximation of fixed points of nonexpansive mappings and quasinonexpansive mappings in a Hilbert space

In this paper, we give a simple proof and some generalizations of results in Falset, Llorens-Fuster, Marino, and Rugiano (2016).Comment: 8 page

Psychological impact and recovery after involvement in a patient safety incident: A repeated measures analysis

OBJECTIVE: To examine individual, situational and organisational aspects that influence psychological impact and recovery of a patient safety incident on physicians, nurses and midwives. DESIGN: Cross-sectional, retrospective surveys of physicians, midwives and nurses. SETTING: 33 Belgian hospitals. PARTICIPANTS: 913 clinicians (186 physicians, 682 nurses, 45 midwives) involved in a patient safety incident. MAIN OUTCOME MEASURES: The Impact of Event Scale was used to retrospectively measure psychological impact of the safety incident at the time of the event and compare it with psychological impact at the time of the survey. RESULTS: Individual, situational as well as organisational aspects influenced psychological impact and recovery of a patient safety incident. Psychological impact is higher when the degree of harm for the patient is more severe, when healthcare professionals feel responsible for the incident and among female healthcare professionals. Impact of degree of harm differed across clinicians. Psychological impact is lower among more optimistic professionals. Overall, impact decreased significantly over time. This effect was more pronounced for women and for those who feel responsible for the incident. The longer ago the incident took place, the stronger impact had decreased. Also, higher psychological impact is related with the use of a more active coping and planning coping strategy, and is unrelated to support seeking coping strategies. Rendered support and a support culture reduce psychological impact, whereas a blame culture increases psychological impact. No associations were found with job experience and resilience of the health professional, the presence of a second victim support team or guideline and working in a learning culture. CONCLUSIONS: Healthcare organisations should anticipate on providing their staff appropriate and timely support structures that are tailored to the healthcare professional involved in the incident and to the specific situation of the incident

Resource Saving via Ensemble Techniques for Quantum Neural Networks

Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conduct experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices.Comment: Extended paper of the work presented at QTML 2022. Close to published versio

Gait Monitoring and Analysis: A Mathematical Approach

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson’s disease by monitoring their gait due to wearable devices that can accurately detect a person’s movements. In our study, about 50 people were involved in the trial (20 with Parkinson’s disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the “gait quality” based on the measure of entropy obtained by applying the Fourier transform

Duration of second victim symptoms in the aftermath of a patient safety incident and association with the level of patient harm: A cross-sectional study in the Netherlands

OBJECTIVES: To describe healthcare providers' symptoms evoked by patient safety incidents (PSIs), the duration of these symptoms and the association with the degree of patient harm caused by the incident. DESIGN: Cross-sectional survey. SETTING: 32 Dutch hospitals that participate in the 'Peer Support Collaborative'. PARTICIPANTS: 4369 healthcare providers (1619 doctors and 2750 nurses) involved in a PSI at any time during their career. INTERVENTIONS: All doctors and nurses working in direct patient care in the 32 participating hospitals were invited via email to participate in an online survey. PRIMARY AND SECONDARY OUTCOME MEASURES: Prevalence of symptoms, symptom duration and its relationship with the degree of patient harm. RESULTS: In total 4369 respondents were involved in a PSI and completely filled in the questionnaire. Of these, 462 reported having been involved in a PSI with permanent harm or death during the last 6 months. This had a personal, professional impact as well as impact on effective teamwork requirements. The impact of a PSI increased when the degree of patient harm was more severe. The most common symptom was hypervigilance (53.0%). The three most common symptoms related to teamwork were having doubts about knowledge and skill (27.0%), feeling unable to provide quality care (15.6%) and feeling uncomfortable within the team (15.5%). PSI with permanent harm or death was related to eightfold higher likelihood of provider-related symptoms lasting for more than 1\u2009month and ninefold lasting longer than 6\u2009months compared with symptoms reported when the PSI caused no harm. CONCLUSION: The impact of PSI remains an underestimated problem. The higher the degree of harm, the longer the symptoms last. Future studies should evaluate how these data can be integrated in evidence-based support systems

Safety of SARS-CoV2 vaccination and COVID-19 short-term outcome in pediatric acquired demyelinating disorders of central nervous system:A single center experience

IntroductionConcern of a correlation between disease relapse in patients with acquired demyelinating disorders of central nervous system (CNS) and SARS-CoV2 vaccines has been raised. In this single center study, we retrospectively evaluated safety of SARS-CoV2 vaccination and COVID-19 short-term outcome in pediatric acquired demyelinating disorders of CNS.Materials and methodsPatients with multiple sclerosis (MS), myelin oligodendrocyte glycoprotein antibody associated disease (MOGAD) and neuromyelitis optica spectrum disorder (NMOSD) with disease onset before 18 years of age were included. Demographic and clinical data, and information regarding previous SARS-CoV-2 infection and vaccination were collected.ResultsWe included nine patients with MOGAD. Six patients received SARS-CoV2 vaccination and complained pain at injection site while only one had fever and fatigue. Median follow-up was 28 weeks (range 20-48). Seven patients had COVID-19 occurring with mild flu-like symptoms and median follow-up was 28 weeks (range 24-34). Nobody had disease relapse. Five patients with NMOSD were included. All patients received SARS-CoV2 vaccination (BNT162b2-Pfizer-BioNTech). The median follow-up was 20 weeks (range 14-24) and only two patients complained pain at injection site, fever and fatigue. Three patients had also COVID-19 with mild flu-like symptoms, despite two of them being under immunosuppressive treatment. Lastly, forty-three patients with MS were included. 35 out of 43 received SARS-CoV2 vaccination with a median follow-up of 24 weeks (range 8-36). Fourteen patients had no side effects, while 21 complained mild side effects (mainly pain at injection site) and one experienced a disease relapse with complete recovery after steroid therapy. At vaccination, all but one were under treatment. Sixteen patients had COVID-19 occurring with mild symptoms.DiscussionCOVID-19 outcome was good although many patients were under immunosuppressive treatment. Vaccine-related side effects were frequent but were mild and self-limited. Only one MS patient had a post-vaccination relapse with complete recovery after steroid therapy. In conclusion, our data support the safety of SARS-CoV-2 vaccines in pediatric MS, MOGAD and NMOSD

A cluster randomized trial to assess the impact of clinical pathways for patients with stroke: rationale and design of the Clinical Pathways for Effective and Appropriate Care Study [NCT00673491]

<p>Abstract</p> <p>Background</p> <p>Patients with stroke should have access to a continuum of care from organized stroke units in the acute phase, to appropriate rehabilitation and secondary prevention measures. Moreover to improve the outcomes for acute stroke patients from an organizational perspective, the use of multidisciplinary teams and the delivery of continuous stroke education both to the professionals and to the public, and the implementation of evidence-based stroke care are recommended. Clinical pathways are complex interventions that can be used for this purpose. However in stroke care the use of clinical pathways remains questionable because little prospective controlled data has demonstrated their effectiveness. The purpose of this study is to determine whether clinical pathways could improve the quality of the care provided to the patients affected by stroke in hospital and through the continuum of the care.</p> <p>Methods</p> <p>Two-arm, cluster-randomized trial with hospitals and rehabilitation long-term care facilities as randomization units. 14 units will be randomized either to arm 1 (clinical pathway) or to arm 2 (no intervention, usual care). The sample will include 238 in each group, this gives a power of 80%, at 5% significance level. The primary outcome measure is 30-days mortality. The impact of the clinical pathways along the continuum of care will also be analyzed by comparing the length of hospital stay, the hospital re-admissions rates, the institutionalization rates after hospital discharge, the patients' dependency levels, and complication rates. The quality of the care provided to the patients will be assessed by monitoring the use of diagnostic and therapeutic procedures during hospital stay and rehabilitation, and by the use of key quality indicators at discharge. The implementation of organized care will be also evaluated.</p> <p>Conclusion</p> <p>The management of patients affected by stroke involves the expertise of several professionals, which can result in poor coordination or inefficiencies in patient treatment, and clinical pathways can significantly improve the outcomes of these patients. It is proposed that this study will test a new hypothesis and provide evidence of how clinical pathways can work.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov ID [NCT00673491]</p