Hepatitis of Unknown Origin and Etiology (Acute Non HepA-E Hepatitis) among Children in 2021/2022: Review of the Current Findings
Bibliographic record
Abstract
Several clusters and individual cases of acute hepatitis have been reported in the US, Europe and recently in Asia and Central America since October 2021. A laboratory investigation of the common viral hepatitis agents (HAV, HBV, HCV, HDV and HEV) yielded negative results prompting the use of the term “acute non HepA-E hepatitis” to describe this condition. The cases were characterized by the manifestations of acute hepatitis (abdominal pain, vomiting, diarrhea, jaundice and very high levels of liver enzymes) affecting children with a median age of 3–4 years. The exact underlying etiology has not been revealed yet; however, a leading hypothesis is that an infectious agent is the culprit, underlying cause or a risk factor for acute non HepA-E hepatitis occurrence. So far, laboratory testing has shown the presence of the group F human adenovirus serotype 41 (HAdV-F41) in about three-fourths of the investigated cases. As of 13 May 2022, more than 450 cases were reported worldwide, the majority of which were in the UK (n = 176), the US (n = 109), 13 European countries (at least 103 cases) and in Argentina, Brazil, Canada, Costa Rica, Indonesia, Israel, Japan, Palestine, Panama, Singapore and South Korea. Vigilant surveillance and epidemiologic investigations to identify further cases are warranted to delineate the features of this emergent public health issue. The possible role of environmental and toxic agents including foodborne toxins should also be considered. Specific guidelines for identification of further cases are necessary, particularly in low-income settings where testing for adenoviruses is not considered routinely. A genetic analysis of HAdV-F41 isolates is recommended to assess the potential changes in the virus genome with subsequent possible altered virus behavior. Immunopathogenesis is another possibility that should be evaluated considering the lack of viral structures in liver biopsies of the affected children in the US.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".