Sensitive Detection Assays for Influenza RNA Do Not Reveal Viremia in US Blood Donors
Bibliographic record
Abstract
BACKGROUND: There have been anecdotal reports of influenza viremia since the 1960s. We present an assessment of the prevalence of seasonal and 2009 H1N1 influenza viremia (via RNA testing) in blood donor populations using multiple sensitive detection assays. METHODS: Several influenza RNA amplification assays, including transcription-mediated amplification (TMA) and 2 reverse-transcription polymerase chain reaction (RT-PCR) assays, were evaluated and used to test donor samples. Retrospective samples from 478 subjects drawn at sites with high influenza activity were tested. Prospective samples were collected from 1004 blood donors who called their donation center within 3 days of donation complaining of influenza-like illness (ILI). The plasma collected on the day of donation for these subjects was tested. RESULTS: Of the repository samples, 2 of 478 plasma samples were initially reactive but not repeat reactive by influenza TMA. Of blood donors reporting ILI symptoms postdonation, 1 of 1004 samples was TMA initially reactive but not repeat reactive; all samples were nonreactive by RT-PCR testing. CONCLUSIONS: Targeting blood donor populations most likely to have influenza infection, we failed to detect influenza RNA in 1482 donor samples, with most tested by 3 different RNA assays. Seasonal influenza does not appear to pose a significant contamination threat to the blood supply.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".