Prophylaxis for covid-19: living systematic review and network meta-analysis
Bibliographic record
Abstract
Abstract 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. Data sources WHO covid-19 database, a comprehensive multilingual source of global covid-19 literature to 19 January 2021, and six additional Chinese databases to 20 January 2021. Study selection Randomized trials in which people at risk of covid-19 were randomized to drug 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 first iteration of this living network meta-analysis includes nine randomized trials – six addressing hydroxychloroquine (6,059 participants), one addressing ivermectin combined with iota-carrageenan (234 participants) and two addressing ivermectin alone (540 participants), all compared to standard care or placebo. Hydroxychloroquine has no important effect on admission to hospital (risk difference (RD) 1 fewer per 1,000, 95% credible interval (CrI) 3 fewer to 4 more, high certainty) or mortality (RD 1 fewer per 1,000, 95% CrI 2 fewer to 3 more, high certainty). Hydroxychloroquine probably has no important effect on laboratory-confirmed infection (RD 2 more per 1,000, 95% CrI 18 fewer to 28 more, moderate certainty), probably increases adverse effects leading to drug discontinuation (RD 19 more per 1,000, 95% CrI 1 fewer to 70 more, moderate certainty) and may have no important effect on suspected, probable or laboratory-confirmed infection (RD 15 fewer per 1,000, 95% CrI 64 fewer to 41 more, low certainty). Due to serious risk of bias and very serious imprecision – and thus very low certainty evidence, the effects of ivermectin combined with iota-carrageenan on laboratory-confirmed infection (RD 52 fewer per 1,000, 95% CrI 58 fewer to 37 fewer), and ivermectin alone on laboratory-confirmed infection (RD 50 fewer per 1,000, 95% CrI 59 fewer to 16 fewer) and suspected, probable or laboratory-confirmed infection (RD 159 fewer per 1,000, 95% CrI 165 fewer to 144 fewer) remain uncertain. Conclusion Hydroxychloroquine prophylaxis does not have an important effect on hospital admission and mortality, probably increases adverse effects, and probably does not have an important effect on laboratory-confirmed SARS-CoV-2 infection. Because of serious risk of bias and very serious imprecision, we are highly uncertain whether ivermectin combined with iota-carrageenan and ivermectin alone reduce the risk of SARS-CoV-2 infection. 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). Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication.
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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 | 0.032 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".