Medicine
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Meta Analysis
The association of CDKN2BAS gene polymorphisms and intracranial aneurysm: A meta-analysis.
Intracranial aneurysm (IA) is one of the main causes of subarachnoid hemorrhage (SAH) leading to a high percentage of disability and mortality worldwide. In addition to environmental factors, the risk of rupture or prognosis of intracranial aneurysm is also closely related to gene. ⋯ Therefore, we performed a meta-analysis of CDKN2BAS SNPs to explore its association with intracranial aneurysms and the results show a significance relation between rs10757272, rs1333040, and rs6475606 with intracranial aneurysm. This will open a new perspective for future intracranial aneurysm gene research and therapy.
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Metatarsalgia refers to localized or generalized forefoot pain in the region of the metatarsal heads. Often this pain is plantar, beneath the metatarsal heads, and arises from either mechanical or iatrogenic causes. The treatment of metatarsalgia remains controversial. A thorough understanding of the biomechanics of the forefoot and the underlying pathology of the particular type of metatarsalgia affecting the patient is a prerequisite to selecting the proper treatment. In recent years, massage therapy has been increasingly accepted by patients due to its lower costs, fewer unwanted side effects, and safety for clinical use. In this systematic review, we aim to evaluate the effectiveness and safety of massage therapy for patients with metatarsal pain. ⋯ DOI 10.17605/OSF.IO/C6KFJ.
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Breast cancer is the most familiar cancer and the major cause of the cancer death in women worldwide. The breast cancer patients may suffer from severe mental and physical trauma. At present, there are few studies on the music therapy for patients with breast cancer. The objective of our paper is to assess the effect of music intervention on mental and physical state of breast cancer patients. ⋯ Music intervention can improve the mental and physical health of the breast cancer patients.
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Observational Study
The risk factors analysis and establishment of an early warning model for healthcare-associated infections after pediatric cardiac surgery: A STROBE-compliant observational study.
The aim of this study was to identify the main risk factors for health-care-associated infections (HAIs) following cardiac surgery and to establish an effective early warning model for HAIs to enable intervention in an earlier stage. In total, 2227 patients, including 222 patients with postoperative diagnosis of HAIs and 2005 patients with no-HAIs, were continuously enrolled in Beijing Anzhen Hospital, Beijing, China. Propensity score matching was used and 222 matched pairs were created. ⋯ The ROC showed the area under curve was 0.985 (confidence interval: 0.975-0.996). When the probability was 0.529, the model had the highest prediction rate, the corresponding sensitivity was 0.946, and the specificity was 0.968. According to the results, the early warning model containing medium to high complexity, intubation time, urinary catheter time, and central venous catheter time enables more accurate predictions and can be used to guide early intervention after pediatric cardiac surgery.
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Observational Study
Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.
Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work was to develop a ML model to predict 30-day all-cause rehospitalizations based on the French hospital medico-administrative database. ⋯ In contrast, LR was superior to CART (H-measure = 0.16, AUC = 0.70), P < .0001. The use of ML may be an alternative to regression models to predict health outcomes. The integration of ML, particularly the RF algorithm, in the prediction of unplanned rehospitalization may help health service providers target patients at high risk of rehospitalizations and propose effective interventions at the hospital level.