Clinical Utility of Commercial Enzyme Immunoassays during the Inaugural Season of West Nile Virus Activity, Alberta, Canada
Bibliographic record
Abstract
West Nile virus (WNV) has spread rapidly across North America, creating a need for rapid and accurate laboratory diagnosis on a large scale. Immunoglobulin M (IgM) capture enzyme immunoassays (EIA) became commercially available in the summer of 2003, but limited data are available on their clinical performance. Consolidated human WNV diagnostic testing for the province of Alberta, Canada, at the public health laboratory permitted a large-scale evaluation of the assays, covering a wide clinical spectrum. Two thousand nine hundred sixty-nine sera were tested, from 2,553 Alberta residents, and 266 cases were identified. Sensitivities of the Focus assay and first-generation Panbio IgM capture EIA were 79 and 80%, respectively. During the first week of illness only 53 to 58% of cases were positive, but sensitivity was 96 to 97% after day 8. Sensitivity for neurological cases was 92% overall. Specificity was high for the Focus kit at 98.9%, but only 82.9% for the first Panbio kit. A positive Focus WNV IgG result with a twofold rise in IgG index was a reliable indicator of acute flavivirus infection (67/67 WNV). Agreement between the IgG test and hemagglutinin inhibition titers in paired sera was at least 82%. Commercial IgM and IgG EIA proved useful for WNV diagnosis, provided follow-up sera were collected after 8 days of illness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".