Bibliographic record
Abstract
Ebola viruses can cause severe hemorrhagic fever in humans and nonhuman primates with fatality rates up to 90%, and are identified as biosafety level 4 pathogens and CDC Category A Agents of Bioterrorism. To date, there are no approved therapies and vaccines available to treat these infections. Antibody therapy was estimated to be an effective and powerful treatment strategy against infectious pathogens in the late 19th, early 20th centuries but has fallen short to meet expectations to widely combat infectious diseases. Passive immunization for Ebola virus was successful in 2012, after over 15 years of failed attempts leading to skepticism that the approach would ever be of potential benefit. Currently, monoclonal antibody (mAbs)-based therapies are the most efficient at reversing the progression of a lethal Ebola virus infection in nonhuman primates, which recapitulate the human disease with the highest similarity. Novel combinations of mAbs can even fully cure lethally infected animals after clinical symptoms and circulating virus have been detected, days into the infection. These new developments have reopened the door for using antibody-based therapies for filovirus infections. Furthermore, they are reigniting hope that these strategies will contribute to better control the spread of other infectious agents and provide new tools against infectious diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".