The first RT-qPCR confirmed case of tick-borne encephalitis in a dog in Scandinavia
Bibliographic record
Abstract
BACKGROUND: Tick-borne encephalitis (TBE) is a zoonotic neurological disease caused by tick-borne encephalitis virus (TBEV), a flavivirus endemic in parts of Europe and Asia. Seroconversion without signs of clinical disease is common in dogs and most of the cases previously described have been tentatively diagnosed by combining neurologic signs with serum antibody titres. Here, the first Scandinavian RT-qPCR-confirmed clinical case of TBE in a dog is reported. CASE PRESENTATION: A 4-year old castrated male Pointer Labrador cross was presented with acute-onset ataxia. During hospitalisation, the dog developed seizures. Despite aggressive treatment with steroids, antimicrobials and sedation/anaesthesia, there was continued deterioration during the following 24 h after admission and the dog was euthanised and submitted for necropsy. Histopathological changes in the brain were consistent with lymphoplasmacytic and histiocytic meningoencephalomyelitis. RT-qPCR examination of the brain was positive for TBEV, confirming infection. CONCLUSIONS: Meningoencephalomyelitis caused by TBEV should be a diagnostic consideration in dogs presenting with clinical signs of central nervous system disease such as acute-onset ataxia and seizures in areas where TBEV-positive ticks are endemic. Clinical TBE may be underdiagnosed in dogs due to lack of specific testing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".