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Dry eye disease (DED) is among the most common eye diseases and is becoming increasingly prevalent. Its symptoms cause a long-term decline in patients' health-related quality of life (HRQL). Inconsistencies often occur between the clinical findings and the subjective symptoms of DED. Therefore, a holistic, balanced, and quantitative evaluation of the subjective symptoms and HRQL using patient-reported outcome questionnaires, in addition to clinical findings, is crucial for accurate DED assessment in patients. This paper reviewed the characteristics of current dry eye questionnaires, including their objectives, number of questions, inclusion of HRQL-related items, and whether they were properly evaluated for psychometric properties. Twenty-four questionnaires were identified; among them, the following six questionnaires that included items assessing HRQL and were properly evaluated for psychometric properties are recommended: the Ocular Surface Disease Index, Impact of Dry Eye in Everyday Life, Dry Eye-Related Quality-of-life Score, University of North Carolina Dry Eye Management Scale, Chinese version of Dry Eye-Related Quality of Life, and 25-Item National Eye Institute Visual Function Questionnaire. Dry eye questionnaires have different objectives and are available in different languages. Therefore, medical practitioners should confirm the characteristics of applicable questionnaires before selecting the most appropriate ones. [ABSTRACT FROM AUTHOR]

Structural variations of the thoracic outlet create a unique risk for neurogenic thoracic outlet syndrome (nTOS) that is difficult to diagnose clinically. Common anatomical variations in brachial plexus (BP) branching were recently discovered in which portions of the proximal plexus pierce the anterior scalene. This results in possible impingement of BP nerves within the muscle belly and, therefore, predisposition for nTOS.We hypothesized that some cases of disputed nTOS result from these BP branching variants. We tested the association between BP piercing and nTOS symptoms, and evaluated the capability of ultrasonographic identification of patients with clinically relevant variations. Eighty-two cadaveric necks were first dissected to assess BP variation frequency. In 62.1%, C5, superior trunk, or superior + middle trunks pierced the anterior scalene. Subsequently, 22 student subjects underwent screening with detailed questionnaires, provocative tests, and BP ultrasonography. Twenty-one percent demonstrated atypical BP branching anatomy on ultrasound; of these, 50% reported symptoms consistent with nTOS, significantly higher than subjects with classic BP anatomy (14%). This group, categorized as a typical TOS, would be missed by provocative testing alone. The addition of ultrasonography to nTOS diagnosis, especially for patients with BP branching variation, would allow clinicians to visualize and identify atypical patient anatomy. [ABSTRACT FROM AUTHOR]

In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer's disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions. [ABSTRACT FROM AUTHOR]