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 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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".