Ebola Virus Disease: Therapeutic and Potential Preventative Opportunities
Bibliographic record
Abstract
The 2014 Ebola virus disease (EVD) epidemic in West Africa was unprecedented in its geographical distribution, scale, and toll on public health infrastructure. Standard public health measures were rapidly overwhelmed, and many projections on outbreak progression through the region were dire. At the beginning of the outbreak there were no treatments or vaccines that had been shown to be safe and effective for treating or preventing EVD, limiting health care providers to offer supportive care under extremely challenging circumstances and at great risk to themselves. Over time, however, drugs and vaccines in the development pipeline were prioritized based on all available research data and were moved forward for evaluation in clinical trials to demonstrate safety and efficacy. The armamentarium against EVD eventually included biologics such as monoclonal antibodies, convalescent plasma, and vaccines as well as small molecule therapeutics such as small interfering RNAs and nucleoside analogs. This article provides a high-level overview of the interventions and prophylactics considered for use in the outbreak and discusses the challenges faced when attempting to deploy investigational countermeasures in the midst of an evolving epidemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".