Collaborative study to evaluate a working reagent for West Nile virus RNA detection by nucleic acid testing
Bibliographic record
Abstract
BACKGROUND: A nucleic acid test (NAT) assay reference reagent for West Nile virus (WNV) RNA, consisting of heat-inactivated WNV grown in tissue culture and diluted in pooled, negative human plasma, was evaluated and quantitated in a collaborative study in which 14 laboratories participated. STUDY DESIGN AND METHODS: Participants were requested to assay serial half-log and log dilutions of the reagent to determine the RNA endpoint. A single endpoint for each such dilution series was calculated with the maximum likelihood method, which assumes that the probability of a positive result at a given dilution follows a Poisson distribution. The calculated endpoint was used to give an estimated "NAT-detectable units per mL" (not necessarily equivalent to genome equivalents/mL or copies/mL). The assays used by participants included qualitative and quantitative NAT assays and both the commercial WNV assays (Chiron and Roche). RESULTS: The estimated number of detectable units per mL for the 14 laboratories varied from log 2.0 to 3.0 with the exception of two outliers. The overall mean titer for all the assays was log 2.52 detectable units per mL (330 detectable units/mL). Multiple testing of individual vials by two laboratories indicated that there was no evidence of vial-to-vial variation in WNV content of the reference reagent. CONCLUSION: A reference reagent for WNV NAT assays has been established. The mean titer of the reagent, with the results from 14 laboratories, was 330 detectable units per mL.
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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 | 0.117 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".