Mortality rates and causes of death among all HIV-positive individuals with hemophilia in Canada over 21 years of follow-up
Bibliographic record
Abstract
Many individuals with hemophilia were infected with human immunodeficiency virus (HIV) in the early 1980s through contaminated blood products. Most also were co-infected with hepatitis C virus (HCV). Deaths among the entire cohort of HIV-positive hemophiliacs in Canada up to 2003 are described. Using registry data, we analyzed Kaplan-Meier survival curves, determined the effect of age at HIV seroconversion on mortality, and described cause-specific proportional mortality patterns over time. Of 2427 Canadians with hemophilia, 660 (27.2%) were HIV-positive, of whom 406 (61.5%) died. In contrast, 114 (6.5%) deaths occurred in HIV-negative controls. Median age at HIV seroconversion was 20 (range, < 1-67 years), and median survival was 15.0 years (95% confidence interval, 13.6-16.4 years). Younger age at HIV seroconversion was associated with improved survival; however, this finding was not explained by differences in causes of death across age groups. Following the introduction of highly active antiretroviral therapy, the proportion of deaths due to acquired immune deficiency syndrome has decreased, while the proportion of deaths due to liver disease has increased. There were 1134 HCV-positive individuals, of whom only 444 (39.2%) were also HIV-positive. Liver disease is a growing health concern among many hemophiliacs, not only those who are HIV-positive.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".