Prevalence of neurodevelopmental disorders in children with chronic hepatitis C in British Columbia
Bibliographic record
Abstract
Background: Chronic hepatitis C (HCV) in children is primarily acquired through vertical transmission from an HCV-infected mother. The primary risk factor for chronic HCV in mothers is intravenous drug use, which, in pregnancy, is associated with increased risk of neurodevelopmental disorders in children. We explored the prevalence of neurodevelopmental disorders among children with chronic HCV in British Columbia. Methods: Retrospective chart review was conducted for children actively followed for HCV at our centre between December 2020 and April 2024. Data were collected on demographic and clinical information including diagnoses of neurodevelopmental disorders and other learning difficulties. Results: We identified 29 children under 18 years of age (13 male, 16 female); 28 had HCV due to vertical transmission and 1 for unknown reasons, potentially due to the patient's own substance use. Forty-eight percent (n = 14) of patients had attention deficit hyperactivity disorder (ADHD). Among these, 43% (n = 6) had fetal alcohol spectrum disorder, 21% ( n = 3) had autism spectrum disorder, and 14% (n = 2) had other learning and developmental difficulties including speech delay and sensory processing issues. In total, 62% (n = 18) of patients had some neurodevelopmental disorder or learning difficulty. Conclusions: In our study population of children with chronic HCV, a majority had at least one documented neurodevelopmental disorder, representing a previously undescribed issue among pediatric patients with chronic HCV. We suggest practitioners who care for children with chronic HCV may have a role in proactively identifying patients with neurodevelopmental difficulties and linking them with appropriate resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".