Prognosis of hepatitis C virus‐infected Canadian post‐transfusion compensation claimant cohort
Bibliographic record
Abstract
Accurate prognostic estimates were required to ensure the sufficiency of the $1.1 billion compensation fund established in 1998 to compensate Canadians who acquired hepatitis C virus (HCV) infection through blood transfusion between 1986 and 1990. This article reports the application of Markov modelling and epidemiological methods to estimate the prognosis of individuals who have claimed compensation. Clinical characteristics of the claimant cohort (n = 5004) were used to define the starting distribution. Annual stage-specific transition probabilities (F0-->F1, . . ., F3-->F4) were derived from the claimants, using the Markov maximum likelihood estimation method. HCV treatment efficacy was derived from the literature and practice patterns were estimated from a national survey. The estimated stage-specific transition probabilities of the cohort between F0-->F1, F1-->F2, F2-->F3 and F3-->F4 were 0.032, 0.137, 0.150 and 0.097 respectively. At 20 years after the index transfusion, approximately 10% of all living claimants (n = 3773) had cirrhosis and 0.5% developed hepatocellular carcinoma (HCC). For nonhaemophilic patients, the predicted 20-year (2030) risk of HCV-related cirrhosis was 23%, and the risk of HCC and liver-related death was 7% and 11% respectively. Haemophilic patients who are younger and are frequently co-infected with human immunodeficiency virus would have higher 20-year risks of cirrhosis (37%), HCC (12%) and liver-related death (19%). Our results indicate that rates of progression to advanced liver disease in post-transfusion cohorts may be lower than previously reported. The Canadian post-transfusion cohort offers new and relevant prognostic information for post-transfusion HCV patients in Canada and is an invaluable resource to study the natural history and resource utilization of HCV-infected individuals in future studies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".