Background The respiratory illness caused by SARS‐CoV‐2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID‐19). In this update, we include new relevant studies, and have removed studies with case‐control designs, and those not intended to be diagnostic test accuracy studies. Objectives To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X‐ray and ultrasound) in people with suspected COVID‐19. Search methods We searched the COVID‐19 Living Evidence Database from the University of Bern, the Cochrane COVID‐19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID‐19 publications through to 30 September 2020. We did not apply any language restrictions. Selection criteria We included studies of all designs, except for case‐control, that recruited participants of any age group suspected to have COVID‐19 and that reported estimates of test accuracy or provided data from which we could compute estimates. Data collection and analysis The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS‐2 domain‐list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta‐analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs). Main results We included 51 studies with 19,775 participants suspected of having COVID‐19, of whom 10,155 (51%) had a final diagnosis of COVID‐19. Forty‐seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT‐PCR as the reference standard for the diagnosis of COVID‐19, with 47 studies using only RT‐PCR and four studies using a combination of RT‐PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow‐up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty‐two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivit...

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Background: The COVID-19 pandemic has resulted in over 1,000,000 cases across 181 countries worldwide. The global impact of COVID-19 has resulted in a surge of related research. Researchers have turned to social media platforms, namely Twitter, to disseminate their articles. The online database Altmetric is a tool which tracks the social media metrics of articles and is complementary to traditional, citation-based metrics. Citation-based metrics may fail to portray dissemination accurately, due to the lengthy publication process. Altmetrics are not subject to this time-lag, suggesting that they may be an effective marker of research dissemination during the COVID-19 pandemic. Objectives: To assess the dissemination of COVID-19 articles as measured by Twitter dissemination, compared to traditional citation-based metrics, and determine article characteristics associated with tweet rates. Methods: COVID-19 articles obtained from LitCovid published between January 1st to March 18th, 2020 were screened for inclusion. The following article characteristics were extracted independently, in single: Topic (General Info, Mechanism, Diagnosis, Transmission, Treatment, Prevention, Case Report, and Epidemic Forecasting), open access status (open access and subscription-based), continent of corresponding author (Asia, Australia, Africa, North America, South America, and Europe), tweets, and citations. A sign test was used to compare the tweet rate and citation rate per day. A negative binomial regression analysis was conducted to evaluate the association between tweet rate and article characteristics of interest. Results: 1328 articles were included in the analysis. Tweet rates were found to be significantly higher than citation rates for COVID-19 articles, with a median tweet rate of 1.09 (IQR 6.83) tweets per day and median citation rate of 0.00 (IQR 0.00) citations per day, resulting in a median of differences of 1.09 (95% CI 0.86-1.33, P < .001). 2018 journal impact factors were positively correlated with tweet rate (P < .001). The topics Diagnosis (P = .01), Transmission (P < .001), Treatment (P = .01), and Epidemic Forecasting (P < .001) were positively correlated with tweet rate, relative to Case Report. The following continents of the corresponding author were negatively correlated with tweet rate, Africa (P < .001), Australia (P = .03), and South America (P < .001), relative to Asia. Open access journals were negatively correlated with tweet rate, relative to subscription-based journals (P < .001). Conclusions: COVID-19 articles had significantly higher tweets rates compared to citation rates. This study further identified article characteristics that are correlated with the dissemination of articles on Twitter, such as 2018 journal impact factor, continent of the corresponding author, topic, and open access status. This highlights the importance of altmetrics in periods of rapidly expanding research, such as the COVID-19 pandemic to localize highly disseminated articles. Key words: COVID-19; Coronavirus; SARS-CoV-2; Altmetric; Twitter; Tweet; LitCovid; Citation; social media; research