Acute severe hepatitis of unknown origin in children in Canada
Bibliographic record
Abstract
Background: In spring 2022, a series of reports from the United Kingdom and the United States identified an increase in the incidence of acute severe hepatitis in children.The Public Health Agency of Canada (PHAC) collaborated with provincial/territorial health partners to investigate in Canada.Clinical hepatitis, or inflammation of the liver, is not reportable in Canada, so to determine if an increase was occurring above historical levels, the baseline incidence in Canada was estimated.This article estimates the pre-existing baseline incidence of acute severe hepatitis of unknown origin in children in Canada using administrative databases.It further summarizes the outbreak investigation using information from the national case report forms.Methods: A committee with representatives from PHAC and provincial/territorial health partners was established to investigate current cases in Canada.A national probable case definition and case report form were developed, and intentionally created to be highly sensitive to capture all potential cases for etiological investigations.To estimate a nationally representative baseline incidence, hospitalization data were extracted from the Discharge Abstract Database and was combined with data from Québec from the Ministère de la Santé et des Services sociaux.Results: Twenty-eight probable cases of acute severe hepatitis of unknown origin in children were reported between October 1, 2021, to September 23, 2022, by six provinces: British Columbia=1; Alberta=5; Saskatchewan=1; Manitoba=3; Ontario=14; and Québec=4.The estimated national baseline incidence was an average of 70 cases annually, or 5.8 cases per month.Conclusion: There was no apparent increase above the estimated historical baseline levels.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".