Mcdanielworm7115
An imbalanced dataset is commonly found in at least one class, which are typically exceeded by the other ones. A machine learning algorithm (classifier) trained with an imbalanced dataset predicts the majority class (frequently occurring) more than the other minority classes (rarely occurring). Training with an imbalanced dataset poses challenges for classifiers; however, applying suitable techniques for reducing class imbalance issues can enhance classifiers' performance. In this study, we consider an imbalanced dataset from an educational context. Initially, we examine all shortcomings regarding the classification of an imbalanced dataset. Then, we apply data-level algorithms for class balancing and compare the performance of classifiers. The performance of the classifiers is measured using the underlying information in their confusion matrices, such as accuracy, precision, recall, and F measure. The results show that classification with an imbalanced dataset may produce high accuracy but low precision and recall for the minority class. GSK583 inhibitor The analysis confirms that undersampling and oversampling are effective for balancing datasets, but the latter dominates.
Many studies have explored the association between neuropathy and osteoporosis in patients with diabetes mellitus. However, the results still remain inconsistent and controversial. We aimed to estimate the association between diabetic neuropathy and osteoporosis.
Databases, including PubMed, Embase, Web of Science, the Cochrane library, Chinese Biomedical Literature Database (CBM), and Wanfang, were screened from inception to 30 March 2020. Studies were selected and data were extracted by two independent reviewers. Study characteristics and quality sections were reviewed independently. Pooled ORs and 95% CIs were calculated using random effects model when evidence of heterogeneity was present; otherwise, fixed effects model was used. Meta-regression and subgroup analyses were performed to explore the source of heterogeneity. Sensitivity analysis and publication bias were also tested.
A total of 11 studies with 27,585 participants were included in this analysis which indicated that there was an increasedg studies and to elucidate the underlying biological mechanisms.This study sought to determine whether adding virtual reality (VR) was superior to standard of care alone in facilitating reduction in pain and anxiety among children who underwent intravenous catheterization in the emergency department (ED). Sixty-six children aged 6-16 years who needed intravenous placement received VR, or standard of care in the ED (videos, television, iPad, child life specialist). Outcome measures included change in pain score, level of anxiety, patient and parent satisfaction (pain and anxiety), number of trials, and procedure time. Compared with controls, the intervention group had similar age, sex, number of trials, and anesthetic use. Time of procedure was shorter in the VR group (median 5 min) but this was not statistically significant compared with 7 min for the control group. Pain in the intervention group was lower, even before the procedure. Difference in pain (before and after) and anxiety (after the procedure) were similar in both groups. Satisfaction from anxiety management was higher for the VR group (p less then 0.007) and children rated VR significantly more "fun" (p less then 0.024).Conclusion VR was an effective distraction tool and increased satisfaction from anxiety management for this common pediatric procedure, and should be incorporated in management of anxiety in children in the ED setting.Trial registration clinicaltrials.gov ID NCT03681730, https//clinicaltrials.gov/ct2/show/NCT03681730 What is Known • Virtual reality is an evolving computer technology that shows some promise in the areas of acute and chronic pain management due to its ability to create effective distraction. What is New • We report that among children in the emergency setting with intravenous catheterization, satisfaction from the use of VR for anxiety management should support implementation of VR systems for this procedure.
COVID-19 pandemic has disrupted the global health systems worldwide. According to the tremendous rate of interhuman transmission via aerosols and respiratory droplets, severe measures have been required to contain contagion spread. Accordingly, medical and surgical maneuvers involving the respiratory mucosa and, among them, transnasal transsphenoidal surgery have been charged of maximum risk of spread and contagion, above all for healthcare professionals.
Our department, according to the actual COVID-19 protocol national guidelines, has suspended elective procedures and, in the last month, only three patients underwent to endoscopic endonasal procedures, due to urgent conditions (a pituitary apoplexy, a chondrosarcoma causing cavernous sinus syndrome, and a pituitary macroadenoma determining chiasm compression). We describe peculiar surgical technique modifications and the use of an endonasal face mask, i.e., the nose lid, to be applied to the patient during transnasal procedures for skull base pathologiepreventing or at least reducing aerosol/droplets. The creation of a non-rigid face mask, i.e., the nose lid, allows the comfortable introduction of instruments through one or both nostrils and, at the same time, minimizes the release of droplets from the patient's nasal cavity.
The ideal timing of postoperative imaging after pituitary adenoma surgery has yet to be determined. We reviewed our pituitary database to determine whether timing of routine postoperative imaging has significantly changed patients' clinical course or outcomes.
Retrospective chart review of patients undergoing resection of pituitary adenoma at our university center between 2012 and 2017 was performed. Timing and indication for postoperative imaging, findings of immediate and delayed postoperative imaging, as well as re-operations and radiosurgery details were recorded. Visual functions such as acuity and visual fields were used as clinical outcome indicators. Statistical analysis was run using Microsoft Excel.
Five hundred and nineteen patients were identified; 443 had imaging data in our system and were included in the study. Early (< 90 days) MRIs were obtained in 71 patients and late (≥ 90 days) in 372 patients. We found statistical differences in our demographic groups including larger tumors in the early MRI group (early mean 12.
