Comparison of reverse-transcription real-time PCR and immunohistochemistry for the detection of canine distemper virus infection in raccoons in Ontario, Canada
Bibliographic record
Abstract
Canine distemper virus (CDV) is a widespread morbillivirus that causes subclinical to fatal infections in domestic and wild carnivores. Raccoons ( Procyon lotor) are CDV reservoirs and suffer from associated disease. Aspects of pathogenesis may lead to difficulty in the interpretation of commonly used testing modalities, such as reverse-transcription real-time (RT-rt)PCR and immunohistochemistry (IHC). The reliance upon such tests is greater for wildlife, which are often submitted as carcasses with no clinical history. We compared CDV RT-rtPCR results to immunohistochemistry (the gold standard) in tissues from 74 raccoons. These tests had high kappa agreement (lymph node: 0.9335; lung: 0.8671) and a negative correlation between IHC score and threshold cycle (Ct) value for lymph node and lung (Spearman rank correlation coefficient [ r s ] = −0.8555 and −0.8179, respectively; p < 0.00001). An RT-rtPCR Ct value of 30 in lung and lymph node with sensitivity and specificity of 92.3 and 92.6% and 86.8 and 96.4%, respectively, was suitable for determining CDV involvement. Conjunctival swabs provide an alternative for distemper diagnosis, as there was a strong correlation between Ct values of conjunctival swabs and tissues ( r s = −0.8498, p < 0.00001, n = 46). This information will aid in more efficient and accurate diagnoses in individuals, small-scale outbreaks, and epidemiologic investigations in wildlife.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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".