Prophylaxis for COVID-19: a systematic review
Bibliographic record
Abstract
BACKGROUND: While the landscape of vaccine and treatment candidates against the novel coronavirus disease 2019 (COVID-19) has been reviewed systematically, prophylactic candidates remain unexplored. OBJECTIVES: To map pre- and postexposure prophylactic (PrEP and PEP) candidate for COVID-19. DATA SOURCES: PubMed/Medline, Embase, International Committee of Medical Journal Editors and International Clinical Trials Registry Platform clinical trial registries and medRxiv. STUDY ELIGIBILITY CRITERIA AND PARTICIPANTS: All studies in humans or animals and randomized controlled trials (RCTs) in humans reporting primary data on prophylactic candidates against COVID-19, excluding studies focused on key populations. INTERVENTIONS: PrEP and PEP candidate for COVID-19. METHODS: Systematic review and qualitative synthesis of COVID-19 PrEP and PEP studies and RCTs complemented by search of medRxiv and PubMed and Embase for studies reporting RCT outcomes since systematic review search completion. RESULTS: We identified 13 studies (from 2119 database records) and 117 RCTs (from 5565 RCTs listed in the registries) that met the inclusion criteria. Non-RCT studies reported on cross-sectional studies using hydroxychloroquine (HCQ) in humans (n = 2) or reported on animal studies (n = 7), most of which used antibodies. All five completed RCTs focused on the use of HCQ as either PrEP or PEP, and these and the cross-sectional studies reported no prophylactic effect. The majority of ongoing RCTs evaluated HCQ or other existing candidates including non-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines, anti(retro)virals or use of vitamins and supplements. CONCLUSIONS: The key message from completed studies and RCTs seems to be that HCQ does not work. There is little evidence regarding other compounds, with all RCTs using candidates other than HCQ still ongoing. It remains to be seen if the portfolio of existing molecules being evaluated in RCTs will identify successful prophylaxis against COVID-19 or if there is a need for the development of new candidates.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".