A Meta-Analysis of Rhesus Macaques (<i>Macaca mulatta</i>), Cynomolgus Macaques (<i>Macaca fascicularis</i>), African green monkeys (<i>Chlorocebus aethiops</i>), and Ferrets (<i>Mustela putorius furo</i>) as Large Animal Models for COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Animal models are at the forefront of biomedical research for studies of viral transmission, vaccines, and pathogenesis, yet the need for an ideal large animal model for COVID-19 remains. We used a meta-analysis to evaluate published data relevant to this need. Our literature survey contained 22 studies with data relevant to the incidence of common COVID-19 symptoms in rhesus macaques ( Macaca mulatta ), cynomolgus macaques ( Macaca fascicularis ), African green monkeys ( Chlorocebus aethiops ), and ferrets ( Mustela putorius furo ). Rhesus macaques had leukocytosis on Day 1 after inoculation and pneumonia on Days 7 and 14 after inoculation, in frequencies that were similar enough to humans to reject the null hypothesis of a Fisher exact test. However, the differences in overall presentation of disease were too different from that of humans to successfully identify any of these 4 species as an ideal large animal of COVID-19. The greatest limitation to the current study is a lack of standardization in experimentation and reporting. To expand our understanding of the pathology of COVID-19 and evalu- ate vaccine immunogenicity, we must extend the unprecedented collaboration that has arisen in the study of COVID-19 to include standardization of animal-based research in an effort to find the optimal animal model.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it