Evaluation of a Novel Serology Algorithm to Detect Herpes Simplex Virus 1 or 2 Antibodies
Bibliographic record
Abstract
BACKGROUND: Herpes simplex virus (HSV)-type specific serology (TSS) testing has been commercially available for nearly a decade. Guidelines on appropriate use of such testing exist, including Canadian-based recommendations. Despite this, most Canadian laboratories do not offer HSV-type specific serology and many provide only nontype-specific HSV serology tests. METHODS: At the Alberta Provincial Laboratory, HSV TSS is performed using the following algorithm (termed the Alberta algorithm). Eligible specimens are first tested with a nontype-specific kit (Behring Enzygnost IgG). If positive, sera are then tested using the Focus HerpeSelect-2 assay to establish HSV-2 infection. If the HerpeSelect-2 result is negative, the result is reported as anti-HSV-1 positive. In this study we sought a validation of the Alberta algorithm by testing 344 serum samples with the Behring Enzygnost IgG assay, the type specific Focus HerpeSelect 1 and 2 assays, and by Western blot (WB). RESULTS: Taking the WB as a gold standard, the Behring Enzygnost IgG assay showed a sensitivity of 94% and a specificity of 100%, whereas the Alberta algorithm had a sensitivity of 92% and a specificity of 97% for the detection of HSV-2 antibodies, and a sensitivity of 94% and a specificity of 100% for the detection of HSV-1 antibodies in HSV-2 negative sera. Focus HerpeSelect 1 and HerpeSelect 2 sensitivities against WB were 88% and 91%, whereas specificities were 95% and 97% for HSV-1 and HSV-2, respectively. CONCLUSIONS: The Alberta algorithm was at least equivalent to HerpeSelect 1 and 2 in detecting anti-HSV antibodies. Although sensitivity and specificity were higher, the differences were not statistically significant.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".