Influenza pathogenesis: Lessons learned from animal studies with H5N1, H1N1 Spanish, and pandemic H1N1 2009 influenza
Bibliographic record
Abstract
Because cases of highly pathogenic influenza are rare, no systematic clinical studies have evaluated different therapeutic approaches. Instead, treatment recommendations are aimed at the alleviation of clinical signs and symptoms, especially the restoration of respiratory function, and at the inhibition of virus replication, assuming viral load is responsible for disease phenotype. Studies of highly pathogenic influenza in different animal models, especially nonhuman primates and ferrets, reproduce many of the key observations from clinical cases. Host-response kinetics reveal a delayed but broad activation of genes involved in the innate and acquired immune responses (innate responses produce inflammatory responses), which continue after the virus has been cleared and may contribute importantly to the clinical signs observed. Experimental animal models point to an important role for immune dysregulation in the pathogenesis of highly pathogenic influenza. The use of these models to develop and validate therapeutic approaches is just beginning, but published studies reveal the importance of early treatment with antivirals and show the potential and limitations of approaches aimed at the host response.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| 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".