Long‐term morbidity and mortality in a Canadian post‐transfusion hepatitis C cohort: Over 15 years of follow‐up
Bibliographic record
Abstract
The Federal Government of Canada established a $1.1 billion compensation programme in 1999 to support individuals who acquired hepatitis C virus (HCV) through blood products between January 1986 and July 1990. We aimed to describe the morbidity and mortality of this unique post-transfusion cohort (n = 4550) followed for over 15 years from 2000 to 2016. The age-standardized mortality rates were compared with that of the Canadian general population and HCV cohorts from other countries. We evaluated all-cause mortality using Kaplan-Meier survival curves and HCV-related and unrelated mortality using competing risk models. The age-standardized all-cause and HCV-related mortality rates per 10 000 person-years were 127 (95% CI: 117-138) and 76 (95% CI: 69-85) for males, and 77 (95% CI: 69-87) and 43 (95% CI: 37-51) for females, respectively. The risk of death of the post-transfusion cohort was almost twice as high as the Canadian general population (rate ratio = 1.8; 95% CI: 1.7-1.9). All-cause, HCV-related and HCV-unrelated mortality were 20%, 12% and 8%, respectively at 15 years of follow-up. By comparison, HCV-related mortality rates per 10 000 person-years for population-based HCV cohorts varied from 18 and 11 in Australia to 65 and 43 in Scotland for males and females, respectively. We reported long-term follow-up data for the largest post-transfusion cohort in the literature. The all-cause mortality rates were markedly higher than that of the Canadian general population. We also showed that HCV-related mortality were greater compared to other HCV cohorts. This suggests that continued efforts to identify and treat post-transfusion HCV are warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".