West Nile virus testing experience in 2007: evaluation of different criteria for triggering individual‐donation nucleic acid testing
Bibliographic record
Abstract
BACKGROUND: In 2007, clients served by Blood Systems Laboratories used variable approaches for triggering West Nile virus (WNV) RNA individual-donation (ID) nucleic acid testing (NAT). These included two minipool (MP) NAT-reactive donations and a greater than 1:1000 rate in a 7-day interval (primary trigger), criteria based on one MP-NAT-reactive donation when there was WNV activity in overlapping and/or adjacent geographic areas (neighbor trigger), or zero MP-NAT-reactive donation (self-trigger). STUDY DESIGN AND METHODS: The Procleix WNV assay was used in either a 16-sample MP or an ID format. NAT-repeat reactivity or anti-immunoglobulin M (IgM) positivity defined true positives (TPs). TPs that were negative on 1:16 dilution testing were considered ID-NAT yield cases. RESULTS: WNV NAT performed on 1,217,929 donations identified 162 TPs; 87 were detected by MP (rate of 0.008%) and 75 by ID (rate of 0.10%; p < 0.0001). There were 34 ID-NAT yield cases, including 4 IgM/immunoglobulin G (IgG)-negative and 9 IgM-positive/IgG-negative donations. Rates of yield cases by primary, neighbor, and self-triggering were 0.077, 0.052, and 0.004% (p = 0.0003). None of 11 ID-NAT yield cases detected by the neighbor trigger would have been detected if the primary trigger had been used. CONCLUSIONS: Primary triggering criteria identified 21 viremic donations that would have been missed by MP testing; however, 11 other low-level viremic donations required more stringent criteria (e.g., neighbor trigger) for detection. It is reasonable to adopt more stringent ID-NAT triggers, including elimination of the rate criterion and triggering on one NAT-reactive donation for regions adjacent to centers which have already triggered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".