Current therapeutic options for coronavirus disease 2019 (COVID-19)—lessons learned from severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) therapy: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19), also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, first manifested in December 2019, and spread rapidly worldwide. Facing this lethal disease, there is an urgent need to develop potent therapies against SARS-CoV-2 infection. SARS-CoV-2 phylogenetically and symptomatically resembles SARS-CoV and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). Numerous agents have been utilised during the severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) epidemics, which may show some benefit against SARS-CoV-2. METHODS: MEDLINE, EMBASE, Cochrane Library, CBM Disc, China National Knowledge Infrastructure, Wanfang Data, and the China Science and Technology Journal Database will be searched. Manual searches will be conducted by searching pre-printing websites, clinical trial registers, and screening the reference lists of inclusive studies. The screening of all citations and the selection of inclusive articles will be conducted by two reviewers. Randomised controlled trials (RCTs) and controlled cohort studies reporting antiviral therapies, including ribavirin, remdesivir, lopinavir/ritonavir, arbidol, chloroquine, hydroxychloroquine, and interferon, for SARS, MERS, and COVID-19 will be included. The primary outcomes will be mortality, incidence of acute respiratory distress syndrome, and utilisation of mechanical ventilation and intensive care unit admission. The secondary outcomes will be improvement in symptoms and chest radiography results, virus clearance, changes in blood test results, and serum tests. The quality of the retrieved RCTs and observational studies will be appraised according to the Cochrane risk of bias tool and the Newcastle-Ottawa Scale, respectively. If feasible, we will perform a fixed- or random-effects meta-analysis. DISCUSSION: This systematic review and meta-analysis will summarise all the available evidence for the efficacy and safety of current therapeutic options in SARS-CoV, MERS-CoV, or SARS-CoV-2-infected patients. The findings of this study may inform subsequent antiviral interventions for patients with COVID-19. STUDY REGISTRATION: The protocol of this study has been submitted to the PROSPERO platform (https://www.crd.york.ac.uk/PROSPERO/), and the registration number is CRD42020168639.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".