Attribution of Pediatric Acute Gastroenteritis Episodes and Emergency Department Visits to Norovirus Genogroups I and II
Bibliographic record
Abstract
BACKGROUND: Norovirus is a leading cause of acute gastroenteritis. With vaccines in development, population-based estimates of norovirus burden are needed to identify target populations, quantify potential benefits, and understand disease dynamics. METHODS: We estimated the attributable fraction (AF) for norovirus infections in children, defined as the proportion of children testing positive for norovirus whose gastroenteritis was attributable to norovirus. We calculated the standardized incidence and emergency department (ED) visit rates attributable to norovirus using provincial gastroenteritis visit administrative data. RESULTS: From 3731 gastroenteritis case patients and 2135 controls we determined that the AFs were 67.0% (95% confidence interval [CI], 31.5%-100%) and 91.6% (88.8%-94.4%) for norovirus genogroups I (GI) and II (GII), respectively. Norovirus GII AF varied by season but not age. We attributed 116 episodes (95% CI, 103-129) and 59 (51-67) ED visits per 10 000 child-years to norovirus GII across all ages, accounting for 20% and 18% of all medically attended gastroenteritis episodes and ED visits, respectively. CONCLUSIONS: In children, a large proportion of norovirus GII detections reflect causation, demonstrating significant potential for norovirus GII vaccines. Seasonal variation in the norovirus GII AF may have implications for understanding the role asymptomatic carriage plays in disease dynamics.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".