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ticular reduction that is better and more easily executable if malleolar ankle fractures are fixed before intramedullary nailing. In distal tibial fractures CT has always to be performed to recognize associated ankle lesions as it may modify the surgical plan, postoperative management and outcomes.

The decision for revision amputation surgery requires a multi-disciplinary approach and the evidence on outcome data available in the current literature is limited. The aim of this observational, retrospective study was to investigate in patients undergoing stump revision, whether factors such as pathology and reason for the revision surgery affect surgical ‘success’ rates as primary outcomes. Secondary outcomes of interest include rehabilitation outcomes and complication rates.

From December 2008 to November 2018, 250 amputation revision procedures were performed at our centre. Surgical and rehabilitation outcomes and indication for revision surgery were measured for each patient.

There was an overall surgical success rate of 81% and an overall rehabilitation success rate of 63%, with variable outcomes related to the indication for surgery. Revisions for bony pathology had excellent results from a surgical perspective (100%) and good results from a rehabilitation perspective (71%). Revisions for soft t revision surgery for defined anatomical abnormalities, such as bone pathology or neuromata are associated with good outcomes. Surgical techniques, specifically related to the management of neuromas are continuing to develop, with promising results from the application of targeted muscle reinnervation (TMR) in the treatment of neuromata. The primary amputation surgeon should exercise caution when considering compromising bone length for soft tissue or skin coverage, as revision surgery can address the excision of skin graft or refashioning of the soft tissue envelope at a later stage. Careful patient selection is key to ensure we advocate offering our patients the right operation at the right time, for the right pathology.

Although there are increasing concerns on mental health consequences of the COVID-19 pandemic, no large-scale population-based studies have examined the associations of risk perception of COVID-19 with emotion and subsequent mental health.

This study analysed cross-sectional and longitudinal data from the PsyCorona Survey that included 54,845 participants from 112 countries, of which 23,278 participants are representative samples of 24 countries in terms of gender and age. Specification curve analysis (SCA) was used to examine associations of risk perception of COVID-19 with emotion and self-rated mental health. This robust method considers all reasonable model specifications to avoid subjective analytical decisions while accounting for multiple testing.

All 162 multilevel linear regressions in the SCA indicated that higher risk perception of COVID-19 was significantly associated with less positive or more negative emotions (median standardised β=-0.171, median SE=0.004, P<0.001). Specifically, regressions involving economic risk perception and negative emotions revealed stronger associations. EGF816 nmr Moreover, risk perception at baseline survey was inversely associated with subsequent mental health (standardised β=-0.214, SE=0.029, P<0.001). We further used SCA to explore whether this inverse association was mediated by emotional distress. Among the 54 multilevel linear regressions of mental health on risk perception and emotion, 42 models showed a strong mediation effect, where no significant direct effect of risk perception was found after controlling for emotion (P>0.05).

Reliance on self-reported data.

Risk perception of COVID-19 was associated with emotion and ultimately mental health. Interventions on reducing excessive risk perception and managing emotional distress could promote mental health.

Risk perception of COVID-19 was associated with emotion and ultimately mental health. Interventions on reducing excessive risk perception and managing emotional distress could promote mental health.

To assess the #Radiology conversation on Twitter social media platform during the COVID-19 pandemic.

From February 1 to December 31, 2020, all tweets with a #Radiology hashtag were identified using the healthcare social media analytics tool, Symplur Signals. Data collected included number of tweets, retweets, impressions, links, and user characteristics. Data were stratified by the presence of a COVID-19-related keyword, and a social media network analysis was further performed.

Of the 68,172 tweets, 10,093 contained COVID-19 content from 2809 users generating 65,513,669 impressions. More tweets with COVID-19 content contained links than without (P < 0.01). Network analysis demonstrated most users were physicians (48.10%), authoring the most tweets (40.38%), using the most mentions (32.15%), and retweeting the most (51.45%). The most impressions, however, were by healthcare organizations not providing clinical care (20,235,547 impressions, 30.89%). Users came from 80 countries, most from the United States (29.3%) and the United Kingdom (8.69%). During early March, COVID-19 dominated the #Radiology conversation, making up 54.67% of tweets the week of March 14 and 64.74% of impressions the week of March 21 compared to 13.97% of tweets and 16.76% of impressions in the remainder of the study period (P < 0.01).There was an influx of new users to the #Radiology conversation during this time period with more users tweeting about COVID-19 than not (P < 0.01).

Discussion of COVID-19 in the #Radiology community increased significantly during the early weeks of the pandemic. Real time sharing and collaboration proved a useful tool when rapid information dissemination was needed to manage an emerging pathogen.

Discussion of COVID-19 in the #Radiology community increased significantly during the early weeks of the pandemic. Real time sharing and collaboration proved a useful tool when rapid information dissemination was needed to manage an emerging pathogen.

During the COVID-19 pandemic, the rates of food insecurity and mental illness have been projected to increase in the U.S. owing to significant social and economic disruption. This study aims to estimate the prevalence of food insufficiency (often the most extreme form of food insecurity), the correlates of food insufficiency, and the associations between food insufficiency and symptoms of poor mental health in the U.S. during the COVID-19 pandemic.

Cross-sectional data from 63,674 participants of the U.S. Census Household Pulse Survey were collected and analyzed in 2020. Multiple Poisson regression models were used to estimate associations with food insufficiency.

Food insufficiency rose from 8.1% to 10.0% from March to June 2020. Factors associated with food insufficiency included lower age, Black/African American or Latinx race/ethnicity, being unmarried, larger household size, recent employment loss, income below the federal poverty line, and lower education (all p<0.001). Food insufficiency was independently associated with all symptoms of poor mental health, adjusting for socioeconomic and demographic factors (adjusted RRs ranged from 1.