Current incidence and estimated residual risk of transfusion‐transmitted infections in donations made to Canadian Blood Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it