Considerations in the Use of Nonhuman Primate Models of Ebola Virus and Marburg Virus Infection: Table 1.
Bibliographic record
Abstract
The filoviruses, Ebola virus and Marburg virus, are zoonotic pathogens that cause severe hemorrhagic fever in humans and nonhuman primates (NHPs), with case-fatality rates ranging from 23% to 90%. The current outbreak of Ebola virus infection in West Africa, with >26 000 cases, demonstrates the long-underestimated public health danger that filoviruses pose as natural human pathogens. Currently, there are no vaccines or treatments licensed for human use. Licensure of any medical countermeasure may require demonstration of efficacy in the gold standard cynomolgus or rhesus macaque models of filovirus infection. Substantial progress has been made over the last decade in characterizing the filovirus NHP models. However, there is considerable debate over a variety of experimental conditions, including differences among filovirus isolates used, routes and doses of exposure, and euthanasia criteria, all of which may contribute to variability of results among different laboratories. As an example of the importance of understanding these differences, recent data with Ebola virus shows that an addition of a single uridine residue in the glycoprotein gene at the editing site attenuates the virus. Here, we draw on decades of experience working with filovirus-infected NHPs to provide a perspective on the importance of various experimental conditions.
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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 | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".