Scientific reports
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There are limited data pertaining to current practices in timing of surgical decompression for acute thoracolumbar spinal cord injury (SCI). We conducted a retrospective cohort study to evaluate variability in timing between- and within-trauma centers in North America; and to identify patient- and hospital-level factors associated with treatment delay. Adults with acute thoracolumbar SCI who underwent decompressive surgery within five days of injury at participating trauma centers in the American College of Surgeons Trauma Quality Improvement Program were included. ⋯ Moreover, only 7% of surgical timing variability within-centers was explained by case-mix characteristics. The adjusted intraclass correlation coefficient of 12% suggested poor correlation of surgical timing for patients with similar characteristics treated at the same center. These findings support the need for further research into the optimal timing of surgical intervention for thoracolumbar SCI.
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This study examined use trends of e-cigarette devices types, heated tobacco products (HTPs) and e-liquid nicotine concentrations in England from 2016 to 2020. Data were from a representative repeat cross-sectional survey of adults aged 16 or older. Bayesian logistic regression was used to estimate proportions and 95% credible intervals (CrIs). ⋯ Across all years, nicotine concentrations of ≤ 6 mg/ml were most widely (41.0%; 39.4-42.4%) and ≥ 20 mg/ml least widely used (4.1%; 3.4-4.9%). Among e-cigarette/HTP users, ex-smokers were more likely than current smokers to use mod and tank e-cigarettes, but less likely to use pods, disposables, JUUL and HTPs. In conclusion, despite growing popularity of pods and HTPs worldwide, refillable tank e-cigarettes remain the most widely used device type in England.
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Observational Study
Evaluation of a revised resuscitation protocol for out-of-hospital cardiac arrest patients due to COVID-19 safety protocols: a single-center retrospective study in Japan.
This study aimed to determine the association between cardiopulmonary resuscitation (CPR) under the coronavirus 2019 (COVID-19) safety protocols in our hospital and the prognosis of out-of-hospital cardiac arrest (OHCA) patients, in an urban area, where the prevalence of COVID-19 infection is relatively low. This was a single-center, retrospective, observational, cohort study conducted at a tertiary critical care center in Kyoto City, Japan. Adult OHCA patients arriving at our hospital under CPR between January 1, 2019, and December 31, 2020 were included. ⋯ The adjusted odds ratio for hospitalization survival during the COVID-19 safety protocol period was 0.61 (95% CI 0.32-1.18), as compared with conventional CPR. There were no cases of COVID-19 infection among the staff involved in the resuscitation in our hospital. There was no apparent difference in hospitalization survival between the OHCA patients resuscitated under the conventional CPR protocol compared with the current revised protocol for controlling COVID-19 transmission.
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Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. ⋯ We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.
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In the midst of the COVID-19 pandemic, with limited or no supplies of vaccines and treatments, people and policymakers seek easy to implement and cost-effective alternatives to combat the spread of infection during the pandemic. The practice of wearing a mask, which requires change in people's usual behavior, may reduce disease transmission by preventing the virus spread from infectious to susceptible individuals. Wearing a mask may result in a public good game structure, where an individual does not want to wear a mask but desires that others wear it. ⋯ At each time-step, a suspected susceptible individual decides whether to wear a facemask, or not, due to a social learning process that accounts for the risk of infection and mask cost. Numerical results reveal a diverse and rich social dilemma structure that is hidden behind this mask-wearing dilemma. Our results highlight the sociological dimension of mask-wearing policy.