Utilizing blood filter paper and ear punch samples for the detection of rabbit hemorrhagic disease virus 2 by RT-rtPCR
Bibliographic record
Abstract
Rabbit hemorrhagic disease virus 2 (RHDV2), a virulent and contagious viral pathogen that affects wild and domestic lagomorph populations, was identified in Wyoming, USA in December 2020. A surveillance program was developed involving full-carcass submission and liver analysis, although carcass quality as a result of predation and decomposition impeded analysis. To increase the number of submissions and provide flexibility to field staff, we evaluated 2 sample types: 77 dried blood on filter paper samples, 66 ear punch samples. At initial sampling, test specificity and sensitivity of the RT-rtPCR utilizing dried blood on filter paper and ear punch samples were both 100% compared to liver. Filter paper results were consistent over time; sensitivity stayed >96% through weeks 2, 4, and 6, with a maximum mean difference of 6.0 Ct from baseline liver Ct values (95% CI: 5.0-7.3) at 6 wk. Test sensitivity of the ear punch sample at 1, 3, 5, and 7 wk post-sampling remained at 100%, with a maximum mean difference of 5.6 Ct from baseline liver Ct values (95% CI: 4.3-6.9) at 5 wk. Filter paper and ear punch samples were suitable alternatives to liver for RHDV2 surveillance in wild lagomorph populations. Alternative sampling options provide more flexibility to surveillance programs, increase testable submissions, and decrease exposure of field personnel to zoonotic disease agents.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".