Mesenchymal stromal cells for COVID-19: A living systematic review protocol
Rada, G.; Corbalan, J.; Rojas, P.
Show abstract
ObjectiveTo determine the impact of mesenchymal stromal cells outcomes important to patients with COVID-19. DesignThis is the protocol of a living systematic review. Data sourcesWe will conduct searches in PubMed/Medline, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), grey literature and in a centralised repository in L{middle dot}OVE (Living OVerview of Evidence). L{middle dot}OVE is a platform that maps PICO questions to evidence from Epistemonikos database. In response to the COVID-19 emergency, L{middle dot}OVE was adapted to expand the range of evidence it covers and customised to group all COVID-19 evidence in one place. The search will cover the period until the day before submission to a journal. Eligibility criteria for selecting studies and methodsWe adapted an already published common protocol for multiple parallel systematic reviews to the specificities of this question. We will include randomised trials evaluating the effect of mesenchymal stromal cells versus placebo or no treatment in patients with COVID-19. Randomised trials evaluating other coronavirus infections, such as MERS-CoV and SARS-CoV, and non-randomised studies in COVID-19 will be searched in case we find no direct evidence from randomised trials, or if the direct evidence provides low- or very low-certainty for critical outcomes. Two reviewers will independently screen each study for eligibility, extract data, and assess the risk of bias. We will pool the results using meta-analysis and will apply the GRADE system to assess the certainty of the evidence for each outcome. A living, web-based version of this review will be openly available during the COVID-19 pandemic. We will resubmit it every time the conclusions change or whenever there are substantial updates. Ethics and disseminationNo ethics approval is considered necessary. The results of this review will be widely disseminated via peer-reviewed publications, social networks and traditional media. PROSPERO RegistrationSubmitted to PROSPERO (awaiting ID allocation).
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The association between influenza vaccination, all-cause mortality and cardiovascular mortality: a protocol for a living systematic review and prospective meta-analysis 92%
- The incubation period of COVID-19: A rapid systematic review and meta-analysis of observational research 90%
- Comparison of preprints and final journal publications from COVID-19 Studies: Discrepancies in results reporting and spin in interpretation 90%
Similar papers in this journal
- Laboratory-confirmed respiratory viral infection triggers for acute myocardial infarction and stroke: systematic review protocol 90%
- Protocol for the systematic review of the Pneumocystis jirovecii -associated pneumonia in non-HIV immunocompromised patients 90%
- Perioperative mortality in low-, middle-, and high-income countries: Protocol for a multi-level meta-regression analysis 90%
Similar papers in this journal
- Repurposing Existing Medications for Coronavirus Disease 2019: Protocol for a Rapid and Living Systematic Review 91%
- Evidence-Based, Cost-Effective Interventions To Suppress The COVID-19 Pandemic: A Systematic Review 89%
- Direct-acting antivirals for chronic hepatitis C infection: a protocol for a systematic review of observational studies 88%
Similar papers in this journal
- Prophylaxis for covid-19: living systematic review and network meta-analysis 91%
- The TARCiS statement: Guidance on terminology, application, and reporting of citation searching 90%
- Myocarditis and Pericarditis following COVID-19 Vaccination: Rapid Systematic Review of Incidence, Risk Factors, and Clinical Course 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.