Intervertebral disc disease and aortic thromboembolism are the most common causes of acute paralysis in dogs and cats presenting to an emergency clinic
Bibliographic record
Abstract
BACKGROUND: Acute paralysis is a common presentation in small animal emergency clinics, but the aetiological prevalence has not been reported. Knowledge of diagnosis frequency aids prioritisation of differential diagnoses, facilitates resource planning and clinical trial design. METHODS: Medical records from NC State Veterinary Hospital Emergency Room were searched over a five-year period to identify cases presenting with acute non-ambulatory paraparesis or paralysis. Signalment and diagnosis category were extracted. RESULTS: Acute paralysis was the presenting problem in 845 of 21,535 (3.9 per cent) dogs and 66 of 4589 (1.4 per cent) cats admitted over this period. Intervertebral disc disease (IVDD) was the most common cause (608 of 845; 72 per cent) in dogs, followed by vascular disease (34 of 845; 4.0 per cent). Other diagnostic categories accounted for the remaining 20 per cent. Dachshunds were the most common breed (263 of 845; 31.1 per cent), then Labrador retrievers (57 of 845; 6.7 per cent). In cats, aortic thromboembolism (ATE) was the most common diagnosis, occurring in 40 of 66 (60.6 per cent), followed by IVDD (7 of 66; 10.6 per cent). Other diagnostic categories accounted for 30.3 per cent. Six of 845 (0.7 per cent) dogs and two of 66 (3 per cent) cats were categorised as pseudoparalysis with a non-neurological diagnosis. CONCLUSIONS: IVDD and ATE are the overwhelming causes of acute paralysis in dogs and cats, respectively, with approximately 28 per cent of dogs and 40 per cent of cats having a different diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".