Bibliographic record
Abstract
PURPOSE OF REVIEW: Two recent viral epidemics producing pneumonitis (severe acute respiratory syndrome and pandemic influenza A H1N1) have highlighted the potential for viral infections to cause respiratory failure with a significant risk of mortality. This review describes these epidemics and other causes of epidemic viral pneumonia. RECENT FINDINGS: The recent literature highlights the rapidity with which these emerging viral infections can be characterized and how management strategies, including supportive care, antiviral therapy and infection control precautions, can be rapidly shared and implemented. SUMMARY: The severe acute respiratory syndrome outbreak was too short to allow management protocols to be tested in a research environment. The current 2009 influenza A (H1N1) pandemic is fortunately not associated with as high a mortality rate as the avian influenza A (H5N1), another potential pandemic candidate virus. Prior pandemic planning as well as research planning has allowed a rapid response to this outbreak, with a significant amount of literature generated in a few months. Other common seasonal viruses, such as respiratory syncytial virus and parainfluenza, as well as previously poorly recognized viruses such as hantavirus, have the ability to cause significant respiratory morbidity and mortality.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".