Predictors of severe H1N1 infection in children presenting within Pediatric Emergency Research Networks (PERN): retrospective case-control study
Bibliographic record
Abstract
OBJECTIVE: To identify historical and clinical findings at emergency department presentation associated with severe H1N1 outcome in children presenting with influenza-like illness. DESIGN: Multicentre retrospective case-control study. SETTING: 79 emergency departments of hospitals associated with the Pediatric Emergency Research Networks in 12 countries. PARTICIPANTS: 265 children (<16 years), presenting between 16 April and 31 December 2009, who fulfilled Centers for Disease Control and Prevention criteria for influenza-like illness and developed severe outcomes from laboratory confirmed H1N1 infection. For each case, two controls presenting with influenza-like illness but without severe outcomes were included: one random control and one age matched control. MAIN OUTCOME MEASURES: Severe outcomes included death or admission to intensive care for assisted ventilation, inotropic support, or both. Multivariable conditional logistic regression was used to compare cases and controls, with effect sizes measured as adjusted odds ratios. RESULTS: 151 (57%) of the 265 cases were male, the median age was 6 (interquartile range 2.3-10.0) years, and 27 (10%) died. Six factors were associated with severe outcomes in children presenting with influenza-like illness: history of chronic lung disease (odds ratio 10.3, 95% confidence interval 1.5 to 69.8), history of cerebral palsy/developmental delay (10.2, 2.0 to 51.4), signs of chest retractions (9.6, 3.2 to 29.0), signs of dehydration (8.8, 1.6 to 49.3), requirement for oxygen (5.8, 2.0 to 16.2), and tachycardia relative to age). CONCLUSION: These independent risk factors may alert clinicians to children at risk of severe outcomes when presenting with influenza-like illness during future pandemics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".