Current incidence and estimated residual risk of transfusion‐transmitted infections in donations made to Canadian Blood Services
Bibliographic record
Abstract
BACKGROUND: New testing methods such as nucleic acid amplification testing (NAT) and chemiluminescent serologic assays have been introduced, more precise estimates for infectious window periods are available, and a new method for estimating the residual risk (RR) of transfusion-transmitted infections (TTIs) has been developed. Thus, available RR estimates for Canada need to be updated. STUDY DESIGN AND METHODS: Incidence rates for known TTI markers were determined for all allogeneic whole-blood donations made to Canadian Blood Services between 2001 and 2005; they were derived from NAT conversions or seroconversions of repeat donors with at least two donations in a 3-year period. RR estimates for human immunodeficiency virus (HIV)-1 and hepatitis C virus (HCV) derived from the classical incidence/window-period model were compared to those obtained by the new method that estimates incidence from NAT-positive, antibody-negative donations (NAT-yield cases) from all donors divided by person-years. RESULTS: With the classical method, the RR of HIV (1 per 7.8 million donations) and HCV (1 per 2.3 million) were low; HBV RR was higher (1 per 153,000). HCV RR was significantly lower when estimated with the new method (1 per 13 million). Eleven HCV NAT-yield cases were predicted by applying the classical method to our seroconversion data but only 2 were observed (p = 0.011). Observed HIV-1 NAT-yield cases (n = 1) matched those predicted (n = 0.7). CONCLUSION: New tests have reduced an already low risk of TTI in Canada. HCV RR estimates by two different methods differed but both were low.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".