West Nile virus in 2006 and 2007: the Canadian Blood Services' experience
Bibliographic record
Abstract
BACKGROUND: The Canadian blood supply has been screened for West Nile virus (WNV) since 2003. A strategy for targeted individual-donation nucleic acid testing (ID-NAT) was implemented in 2004 to identify potentially infectious donations that may be missed by minipool (MP) testing. In 2007, Canada experienced a larger epidemic than in previous years providing an opportunity to evaluate the ID-NAT triggering algorithm in higher-risk areas. STUDY DESIGN AND METHODS: A specially created database and internal-external communication identified regions for targeted ID-NAT using MP and community triggers. WNV-positive donations identified by ID-NAT were reexamined in MP to assess the efficacy of targeted ID-NAT in identifying potentially infectious donations that may have been missed by MP testing. WNV-positive donation data from 2006 and 2007 were analyzed to examine temporal and geographic trends. A telephone survey about symptoms was carried out after the 2007 season. RESULTS: In total 78 WNV-positive donations were identified (66 true-positives and four false-positives being in 2007). Most positive donations were in the late summer, concentrated in the same western provinces as community cases. Fifty-two donations were identified by ID-NAT and 46% were consistently positive in MP. Of the other 54%, 74% were immunoglobulin (Ig)M- and/or IgG-positive. Fifty-six percent of donors experienced mostly mild symptoms before or after donation (but all said they were well at the time of donation). CONCLUSION: WNV-positive donations correspond geographically with the epidemic. MP testing identifies most potentially infectious donations with a smaller potential benefit from targeted ID-NAT. Mild symptoms are common but may not deter donation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".