Notes

Committing suicide between Scottish army masters: follow-up as well as styles.
To assess the feasibility of a modified workflow that uses machine learning and crowdsourcing to identify studies for potential inclusion in a systematic review.

This was a substudy to a larger randomized study; the main study sought to assess the performance of single screening search results versus dual screening. This substudy assessed the performance in identifying relevant randomized controlled trials (RCTs) for a published Cochrane review of a modified version of Cochrane's Screen4Me workflow which uses crowdsourcing and machine learning. We included participants who had signed up for the main study but who were not eligible to be randomized to the two main arms of that study. The records were put through the modified workflow where a machine learning classifier divided the data set into "Not RCTs" and "Possible RCTs." The records deemed "Possible RCTs" were then loaded into a task created on the Cochrane Crowd platform, and participants classified those records as either "Potentially relevant" or "Not relevant" to the review. Using a prespecified agreement algorithm, we calculated the performance of the crowd in correctly identifying the studies that were included in the review (sensitivity) and correctly rejecting those that were not included (specificity).

The RCT machine learning classifier did not reject any of the included studies. In terms of the crowd, 112 participants were included in this substudy. Of these, 81 completed the training module and went on to screen records in the live task. Applying the Cochrane Crowd agreement algorithm, the crowd achieved 100% sensitivity and 80.71% specificity.

Using a crowd to screen search results for systematic reviews can be an accurate method as long as the agreement algorithm in place is robust.

Open Science Framework https//osf.io/3jyqt.
Open Science Framework https//osf.io/3jyqt.Coronavirus-triggered pulmonary and systemic disease, i.e. systemic inflammatory response to virally triggered lung injury, named COVID-19, and ongoing discussions on refining immunomodulation in COVID-19 without COX2 inhibition prompted us to search the related literature to show a potential target (COX2) and a weapon (celecoxib). The concept of selectively targeting COX2 and closely related cascades might be worth trying in the treatment of COVID-19 given the substantial amount of data showing that COX2, p38 MAPK, IL-1b, IL-6 and TGF-β play pivotal roles in coronavirus-related cell death, cytokine storm and pulmonary interstitial fibrosis. Considering the lack of definitive treatment and importance of immunomodulation in COVID-19, COX2 inhibition might be a valuable adjunct to still-evolving treatment strategies. Celecoxib has properties that should be evaluated in randomized controlled studies and is also available for off-label use.
The global push for the use of hydroxychloroquine (HCQ) and chloroquine (CQ) against COVID-19 has resulted in an ongoing discussion about the effectivity and toxicity of these drugs. Recent studies report no effect of (H)CQ on 28-day mortality. We investigated the effect of HCQ and CQ in hospitalized patients on the non-ICU COVID-ward.

A nationwide, observational cohort study was performed in The Netherlands. Hospitals were given the opportunity to decide independently on the use of three different COVID-19 treatment strategies HCQ, CQ, or no treatment. We compared the outcomes between these groups. The primary outcomes were 1) death on the COVID-19 ward, and 2) transfer to the intensive care unit (ICU).

The analysis included 1064 patients from 14 hospitals 566 patients received treatment with either HCQ (n = 189) or CQ (n = 377), and 498 patients received no treatment. In a multivariate propensity-matched weighted competing regression analysis, there was no significant effect of (H)CQ on mortality on tom the regular ward to the ICU. Recent prospective studies have reported on 28-day, all-cause mortality only; therefore, additional prospective data on the early effects of HCQ in preventing transfer to the ICU are still needed.
We critically evaluated the quality of evidence and quality of harm reporting in clinical trials that evaluated the effectiveness of hydroxychloroquine (HCQ) or chloroquine (CQ) for the treatment of coronavirus disease 2019 (COVID-19).

Scientific databases were systematically searched to identify relevant trials of HCQ/CQ for the treatment of COVID-19 published up to 10 September 2020. The Cochrane risk-of-bias tools for randomized trials and non-randomized trials of interventions were used to assess risk of bias in the included studies. 4EGI-1 molecular weight A 10-item Consolidated Standards of Reporting Trials (CONSORT) harm extension was used to assess quality of harm reporting in the included trials.

Sixteen trials, including fourteen randomized trials and two non-randomized trials, met the inclusion criteria. The results from the included trials were conflicting and lacked effect estimates adjusted for baseline disease severity or comorbidities in many cases, and most of the trials recruited a fairly small cohort of patiof a properly designed and reported clinical trial cannot be overemphasized amid the COVID-19 pandemic, and its dismissal could lead to poorer clinical and policy decisions, resulting in wastage of already stretched invaluable health care resources.An important unknown during the coronavirus disease-2019 (COVID-19) pandemic has been the infection fatality rate (IFR). This differs from the case fatality rate (CFR) as an estimate of the number of deaths and as a proportion of the total number of cases, including those who are mild and asymptomatic. While the CFR is extremely valuable for experts, IFR is increasingly being called for by policy makers and the lay public as an estimate of the overall mortality from COVID-19.
Pubmed, Medline, SSRN, and Medrxiv were searched using a set of terms and Boolean operators on 25/04/2020 and re-searched on 14/05/2020, 21/05/2020 and 16/06/2020. Articles were screened for inclusion by both authors. Meta-analysis was performed in Stata 15.1 by using the metan command, based on IFR and confidence intervals extracted from each study. Google/Google Scholar was used to assess the grey literature relating to government reports.

After exclusions, there were 24 estimates of IFR included in the final meta-analysis, from a wide range of countries, published between February and June 2020.
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