Spontaneous clearance of childhood hepatitis C virus infection
Bibliographic record
Abstract
To describe the spontaneous clearance rate of childhood hepatitis C virus (HCV) infection, to determine whether route of transmission affects the clearance rate and to identify other predictors of clearance. Children with chronic hepatitis C were identified between 1990 and 2001. The rate of spontaneous clearance (defined as >or=2 positive anti-HCV antibody test but negative HCV RNA) was calculated using survival analysis. Univariate and multivariate predictor variables [route of transmission, age at infection, age at last follow-up, alanine aminotransferase (ALT) and gender] for clearance were evaluated. Of 157 patients, 28% of children cleared infection (34 transfusional and 10 nontransfusional cases). The 123 transfusional cases were older at time of infection and at follow-up, compared with the 34 nontransfusional cases. Younger age at follow-up (p < 0.0001) and normal ALT levels (p < 0.0001) favoured clearance. Among cases of neonatal infection, 25% demonstrated spontaneous clearance by 7.3 years. The rate of spontaneous clearance of childhood HCV infection was comparable between transfusional and nontransfusional cases. If clearance occurs, it tends to occur early in infection, at a younger age.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".