INFLUENZA EPIDEMIOLOGY AND VACCINATION
Bibliographic record
Abstract
Influenza virus is a major cause of respiratory illness in children, who have the highest attack rates of all age groups; furthermore, children are a main virus transmitter and contribute significantly to the spread of the disease. The impact of influenza on children is often underestimated but the mortality rate can be high. During the 2003–4 influenza season, there were 153 influenza-associated deaths among children in the US; of these, nearly 50% were previously healthy. Influenza is also an important cause of hospitalisation among those under 2 years of age, with a hospitalisation risk similar to that for the elderly and other high-risk groups. In addition, influenza frequently predisposes children under 3 years of age to complications such as acute otitis media and pneumonia. Influenza may also have substantial socioeconomic consequences for household contacts of children, largely related to parental productivity loss. The significant impact of influenza on children calls for effective prevention and management of this illness. Influenza vaccination of young children is currently recommended in the US and Canada, but few European countries have adopted similar policies. Lack of appreciation of the severity of influenza in children is still one of the greatest obstacles for widespread vaccination, but another major concern surrounds the cost-effectiveness of such an intervention. The future challenge to encourage vaccine acceptance is to increase awareness of the impact of influenza in children among both healthcare personnel and parents. There is also a clear need for new influenza vaccines that are even more immunogenic in young children.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".