Mitigation of the threat posed to transfusion by donors traveling to Zika‐affected areas: a Canadian risk‐based approach
Bibliographic record
Abstract
BACKGROUND: The recent spread of the Zika virus to the Americas and the recognition that it can cause severe disease in the developing fetus has prompted the adoption of measures to mitigate the risk that this virus might pose to transfusion safety. In nonendemic countries, the risk to transfusion results from donors traveling to an endemic region. Canada implemented a 21-day temporary deferral for prospective donors who traveled to such regions. We present the rationale for this policy, including a quantitative risk assessment supported by a Monte Carlo simulation. STUDY DESIGN AND METHODS: The model considered the following parameters, each with specified values and ranges: the probability that a donor recently returned from a Zika-endemic region, the duration of travel to this region, the daily risk of acquiring Zika while in an endemic region, and the incubation and viremic periods. We ran the simulation 20 times, each with 10 million iterations. RESULTS: In the absence of any travel deferral, 32 donors (range, 20-46 donors) would be able to donate while still being at risk of transmitting Zika, corresponding to a rate of 1:312,500 (range, 1:217,000 to 1:500,000). None of these donors would be viremic beyond 21 days after returning from their travel, with a risk estimated at less than 1:200,000,000. CONCLUSIONS: A 21-day temporary travel deferral offers an extremely wide margin of safety for the possible transmission of Zika by a donation obtained from someone who recently returned from a country where the virus is circulating.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".