Risk of introducing Zika virus in the Canadian cord blood supply: A risk analysis
Bibliographic record
Abstract
BACKGROUND: In Canada, as many as 24% of mothers are deferred from cord blood (CB) donation due to risk factors for Zika virus (ZIKV). However, the ZIKV epidemic has waned considerably since 2016, and there has not been any report of ZIKV transmission by CB transplantation, which questions this policy. Thus, we performed an analysis of the risk of introducing ZIKV in the CB supply maintained by Héma-Québec (HQ) and Canadian Blood Services (CBS). STUDY DESIGN AND METHODS: This simulation considered the following parameters: the risk of travel exposure in a high-risk ZIKV country, the duration of travel, the daily risk of acquiring ZIKV in a high-risk country, the probability of materno-fetal ZIKV transmission, the probability of asymptomatic fetal viremia, and the probability of sexual transmission. A hundred million Monte Carlo simulations were run. RESULTS: In the most-likely scenario (probability of traveling to a high-risk ZIKV country while pregnant = 0.178), the risk was estimated at 0.9 ZIKV-positive donations per million donations (95% confidence interval [CI] = 0.4-1.6)-or one every 868 years at HQ and one every 453 years at CBS. In the pessimistic model (probability of traveling to a high-risk ZIKV country while pregnant = 0.240), the risk was estimated at 1.2 ZIKV-positive donations per million donations (95% CI = 0.6-2.1)-or one every 644 years at HQ and one every 340 years at CBS. DISCUSSION: We conclude that the risk of introducing ZIKV in the Canadian CB supply is too small to justify maintaining the current policy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".