Performance analysis of rapid diagnostic tests on atypical bovine spongiform encephalopathy
Bibliographic record
Abstract
The preferred method to determine the prevalence of bovine spongiform encephalopathy (BSE) in a country is to use immunology-based rapid-tests. Though these tests are validated to detect C-type BSE disease-associated prion (PrP(sc)), test-specific properties may influence their ability to detect H- and/or L-type BSE PrP(sc), where both are atypical from C-type PrP(sc). Molecular characterization shows atypical BSE PrP(sc) to have a different sensitivity to proteinase activity and different affinities for certain prion-specific antibodies. It is important to understand how atypical BSE PrP(sc) may affect the performance of rapid-tests, which are typically dependent on the use of specific proteases and antibodies. The current study used experimentally generated C-, H-, and L-type BSE PrP(sc) to evaluate 3 tests used in various national BSE surveillance programs: an immunochromatographic assay, a standard sandwich enzyme-linked immunosorbent assay (stndELISA), and a PrP(sc)-conformation-specific ELISA (confELISA). Although BSE PrP(sc) type had some effects on rapid-test performance, analytical sensitivity for atypical BSE PrP(sc) on all 3 platforms was not significantly compromised. When testing for atypical BSE PrP(sc), the 3 tests were able to meet the same requirements that the European Food Safety Authority set when evaluating the tests for C-type BSE PrP(sc).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".