Bibliographic record
Abstract
During annual influenza epidemics, outbreaks of influenza in closed institutions are common. Among healthy children or young adults, such outbreaks are uncommonly associated with serious morbidity or mortality; however, in hospitals and nursing homes, attack rates as high as 60% and case-fatality rates as high as 50% have been reported. Annual influenza vaccination of both patients or residents and hospital and nursing home staff has had a substantial impact on mortality and has reduced the number of outbreaks. Nonpharmacologic interventions (e.g., handwashing and contact isolation of case patients) may reduce the spread of influenza, although evidence for their efficacy is lacking. Nonetheless, long-term care facilities for the elderly population with high vaccination rates and better-than-average infection-control programs have a 25%-50% chance of experiencing an influenza outbreak each year, with an expected resident attack rate of 35%-40%. Thus, antiviral drugs have been increasingly used to mitigate the impact of influenza outbreaks. There are 2 classes of antiviral drugs that are active against influenza: adamantanes and neuraminidase inhibitors. Drugs of the 2 classes appear to be equally effective for the treatment and prophylaxis of susceptible influenza A virus strains. However, adamantanes are not active against influenza B virus, and an increasing proportion of influenza A isolates are resistant to adamantanes. Adamantanes are associated with higher rates of adverse events than are neuraminidase inhibitors. There is substantial evidence that antiviral prophylaxis is effective in terminating outbreaks of seasonal influenza in closed institutions. If stockpiles are adequate, antiviral drugs are likely to be even more important in mitigating the impact of influenza transmission in health care institutions during the next influenza pandemic.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".