Introduction Telemedicine services saw substantial surges in their use during the COVID-19 pandemic due to the lockdowns and characteristics of the pandemic. Therefore, the authors aimed to systematically review the telemedicine services provided during the COVID-19 pandemic and their potential applications. Methods The authors searched PubMed, Scopus, and Cochrane databases on September 14, 2021. Then, the retrieved records underwent two-step title/abstract and full-text screening processes, and the eligible articles were included for qualitative synthesis. Results The review of studies demonstrated that the telephone is listed 38 times, making it the most common technology used in telemedicine. Video conferencing is also mentioned in 29 articles, as well as other technologies: Mobile-health ( n = 15), Virtual reality ( n = 7). According to the findings of the present study, Tele-follow-up ( n = 24), Tele-consulting ( n = 20), Virtual visits (20), and Tele-monitoring ( n = 18) were the most widely used telemedicine applications. Conclusion Telemedicine has been an effective approach to COVID-19 management. Telemedicine technology is going to play a key role in the future of health medicine, patient consultation, and many other extended applications of health care in remote rural locations.
Background: Since Aspirin’s adverse effects are dose-dependent, and evidence supporting the use of low-dose (LD) Aspirin in preventing venous thromboembolism (VTE) after total hip arthroplasty (THA) is weak, the authors do not know what the minimal effective dosage of Aspirin is to prevent VTE. This study aimed to compare the rates of 90-day symptomatic VTE following THA and total knee arthroplasty in healthy patients taking LD Aspirin vs. high-dose (HD) Aspirin for 6 weeks postoperatively. Materials and methods: A prospective cohort of patients with THA and total knee arthroplasty was conducted at two tertiary centres. Symptomatic VTE within 90 days of index arthroplasty was the primary outcome; gastrointestinal bleeding (GIB) and mortality were secondary outcomes. Results: The final analysis included 312 consecutive patients: 158 in the LD group and 154 in the HD group. Two groups were similar regarding preoperative data, including sex, age, BMI, smoking, diabetes mellitus, Hgb and platelet count, and type of surgery. The LD group had one deep vein thrombosis (0.6%), and the HD group had two (1.3%) (P=0.62). Neither group had PTE. Therefore, VTE rates are the same as deep vein thrombosis rates and similar between the groups (0.6% vs. 1.3%, P=0.62) Regarding GIB due to anticoagulant therapy, no patient in the LD group reported GIB, whereas two (1.3%) patients in the HD group reported GIB within 90 days of arthroplasty. GIB rates did not differ significantly between groups (P=0.24). Considering VTE + GIB combined, the HD groups showed a higher rate of complications (N=4, 2.6%) than the LD groups (N=1, 0.6%) but not statistically significant (P=0.21). Conclusions: Prophylactic administration of Aspirin with low doses (81 mg BID) and high doses (325 mg BID) for six weeks is equally effective at reducing VTE in total joint arthroplasty patients and had similar adverse effects. Level of Evidence: Therapeutic Level II
Background: Technologies can predict various aspects of COVID-19, such as early prediction of cases and those at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases and deaths. Herein, we aimed to review the published models and techniques that predict various COVID-19 outcomes and identify their role in the management of the COVID-19. Methods: This study was a review identifying the prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until September 4th, 2021. In addition, Google Scholar was also searched. Results: We have reviewed 59 studies. The authors reviewed prediction techniques in COVID-19 disease management. Studies in these articles have shown that in the section medical setting, most of the subjects were inpatients. In the purpose of the prediction section, mortality was also the most item. In the type of data/predict section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section. Conclusion: Artificial intelligence and machine learning methods were found to be useful in disease control and prevention. They accelerate the process of diagnosis and move toward great progress in emergency circumstances like the COVID-19 pandemic.
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