High titer human immunoglobulin as a specific therapy against West Nile virus encephalitis
Bibliographic record
Abstract
West Nile virus (WNV) is a mosquito-borne disease found most commonly in Africa, west Asia, and the Middle East, where up to 40% of the human population possesses antibodies. It is an emerging disease in the United States, since 1999 and has spread all over the US and Canada. The virus is maintained in nature in a mosquito-bird-mosquito cycle (primarily Culex), with human horses and other animals serving as incidental hosts. WN infection in humans is usually asymptomatic or involves flu like illness but can develop to severe meningo-encephlitis, with symptoms including cognitive dysfunctions, muscle weakness, paralysis and even death. Elderly and depressed immunity factors are at greatest risk of developing severe neurological disease. Studies in animal models have enhanced significantly the understanding of the viral and host factors that determine the pathogenesis and outcome of WNV disease. Currently, vaccines are available for animal use but there is no effective antiviral therapy or human vaccine for WNV infection. Passive administration of antibodies produced from selected donors has shown promising results in animal models.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".