Anti-Ebola therapy for patients with Ebola virus disease: a systematic review
Bibliographic record
Abstract
BACKGROUND: Management of Ebola virus disease (EVD) has historically focused on infection prevention, case detection and supportive care. Several specific anti-Ebola therapies have been investigated, including during the 2014-2016 West African outbreak. Our objective was to conduct a systematic review of the effect of anti-Ebola virus therapies on clinical outcomes to guide their potential use and future evaluation. METHODS: We searched PubMed, EMBASE, Global Health, Cochrane Library, African Index Medicus, WHOLIS (inception-9 April 2018), and trial registries for observational studies or clinical trials, in any language, that enrolled patients with confirmed EVD who received therapy targeting Ebola virus and reported on mortality, symptom duration, or adverse effects. RESULTS: From 11,257 citations and registered trials, we reviewed 55 full-text citations, of which 35 met eligibility criteria (1 randomized clinical trial (RCT), 8 non-randomized comparative studies, 9 case series and 17 case reports) and collectively examined 21 anti-Ebola virus agents. The 31 studies performed during the West African outbreak reported on 4.8% (1377/28616) of all patients with Ebola. The only RCT enrolled 72 patients (0.25% of all patients with Ebola) and compared the monoclonal antibody ZMapp vs. standard care (mortality, 22% vs. 37%; 95% confidence interval for risk difference, - 36 to 7%). Studies of convalescent plasma, interferon-β-1a, favipiravir, brincidofovir, artesunate-amodiaquine and TKM-130803 were associated with at least moderate risk of bias. CONCLUSIONS: Research evaluating anti-Ebola virus agents has reached very few patients with EVD, and inferences are limited by non-randomized study designs. ZMapp has the most promising treatment signal.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".