Bibliographic record
Abstract
The annual attack rate for influenza in children is high, the highest of any age group. It is estimated that 10% to 40% of children are infected with influenza each winter, a figure similar to the attack rate reported for children during the 2009 H1N1 pandemic.1–4 Children with underlying medical conditions bear a disproportionate burden of influenza-related morbidity and mortality.5–7 Two studies in this issue of Pediatrics add to the compelling body of evidence that children with neurologic conditions are at particularly high risk of complications resulting from influenza infection. In the first, Tran and colleagues report that children with underlying neurologic conditions in Canada had an increased risk of ICU admission after either seasonal or pandemic influenza A infection.8 In the second, Blanton et al report that neurologic disorders were identified in nearly half of all pediatric deaths associated with 2009 H1N1 pandemic influenza in the United States.9 An equally important observation from these studies, however, is the significant morbidity associated with influenza infection among children without known risk factors. Half of all hospitalizations from seasonal influenza A during 2004–2009 and almost a third of all deaths during the 2009–2010 pandemic occurred in children … Address correspondence to Paul V. Effler, MD, MPH, Communicable Disease Control Directorate, Department of Health, 227 Stubbs Terrace Road, Shenton Park, Australia. E-mail: pauleffler{at}gmail.com
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".