Prophylaxis against covid-19: living systematic review and network meta-analysis
Bibliographic record
Abstract
UPDATES: This is the second version (first update) of the living systematic review, replacing the previous version (available as a data supplement). When citing this paper please consider adding the version number and date of access for clarity. OBJECTIVE: To determine and compare the effects of drug prophylaxis on severe acute respiratory syndrome coronavirus virus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (covid-19). DESIGN: Living systematic review and network meta-analysis (NMA). DATA SOURCES: World Health Organization covid-19 database, a comprehensive multilingual source of global covid-19 literature to 4 March 2022. STUDY SELECTION: Randomised trials in which people at risk of covid-19 were allocated to prophylaxis or no prophylaxis (standard care or placebo). Pairs of reviewers independently screened potentially eligible articles. METHODS: After duplicate data abstraction, we conducted random-effects bayesian network meta-analysis. We assessed risk of bias of the included studies using a modification of the Cochrane risk of bias 2.0 tool and assessed the certainty of the evidence using the grading of recommendations assessment, development and evaluation (GRADE) approach. RESULTS: The second iteration of this living NMA includes 32 randomised trials which enrolled 25 147 participants and addressed 21 different prophylactic drugs; adding 21 trials (66%), 18 162 participants (75%) and 16 (76%) prophylactic drugs. Of the 16 prophylactic drugs analysed, none provided convincing evidence of a reduction in the risk of laboratory confirmed SARS-CoV-2 infection. For admission to hospital and mortality outcomes, no prophylactic drug proved different than standard care or placebo. Hydroxychloroquine and vitamin C combined with zinc probably increase the risk of adverse effects leading to drug discontinuation—risk difference for hydroxychloroquine (RD) 6 more per 1000 (95% credible interval (CrI) 2 more to 10 more); for vitamin C combined with zinc, RD 69 more per 1000 (47 more to 90 more), moderate certainty evidence. CONCLUSIONS: Much of the evidence remains very low certainty and we therefore anticipate future studies evaluating drugs for prophylaxis may change the results for SARS-CoV-2 infection, admission to hospital and mortality outcomes. Both hydroxychloroquine and vitamin C combined with zinc probably increase adverse effects. SYSTEMATIC REVIEW REGISTRATION: This review was not registered. The protocol established a priori is included as a supplement. FUNDING: This study was supported by the Canadian Institutes of Health Research (grant CIHR-IRSC:0579001321).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".