Bibliographic record
Abstract
Zika virus can be transmitted by transfusion, but the harm caused to recipients is not clear in most cases. It is very likely that the virus could also be transmitted by transplanted organs. Sensitivity to the risk from transfusion is elevated by consideration of possible severe neurologic damage in fetuses. Strategies for dealing with transfusion risk vary with the presence of Zika in the region. In nonendemic areas, risks can be reduced by excluding donors who have exposure through travel or sexual contact with someone at risk. In both endemic and nonendemic areas, the risk can be further reduced by nucleic acid testing of donors, or pathogen reduction of platelet and plasma products. The real risk to the population depends on the frequency of infection as well as the efficacy of these interventions. The interventions chosen will depend on the risk assessment for any situation; in the United States at this time, a combination of travel deferrals, testing, and, to a lesser extent, pathogen reduction is being used, but universal testing of US blood donors under investigational use has been mandated by the US Food and Drug Administration, beginning with states most at risk of local transmission. Canada is largely using travel deferrals. A precautionary approach may be taken; however, a formal decision-making framework has been suggested. The situation globally is clearly very fluid, as the epidemic continues to spread and we continue to learn how to best protect recipients of blood and transplants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".