Hepatitis E in Canadian blood donors
Bibliographic record
Abstract
BACKGROUND: Hepatitis E virus (HEV) is a virus of emerging importance to transfusion medicine as studies on blood donors and other populations demonstrate that the prevalence of endemic cases is higher than previously recognized and the risk to vulnerable transfusion recipients is not insignificant. STUDY DESIGN AND METHODS: We carried out an HEV prevalence study on 13,993 Canadian blood donors with polymerase chain reaction (PCR) testing on all donors and antibody testing on a subset of 4102 donors. HEV antibody-positive and age- and sex-matched antibody-negative donors were invited to participate in a scripted telephone interview about risk factors. RESULTS: There were no PCR-positive samples found (95% confidence interval [CI], 0%-0.026%). The seroprevalence of HEV in our tested population was 5.9% (95% CI, 5.16%-6.59%). HEV antibody positivity was associated with male sex and increasing age. In case-control analysis history of living outside Canada (odds ratio [OR], 2.9; 95% CI, 1.56-5.32) and contact with farm animals (OR, 1.5; 95% CI, 1.01-2.28) were associated with HEV seropositivity. CONCLUSION: This is the largest data set to date on HEV infection in Canada. Results suggest low lifetime exposure to HEV and that infectious donations are rare.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".