Pyrexia in juvenile dogs: a review of 140 referred cases
Bibliographic record
Abstract
OBJECTIVES: To describe the presentation, influence of previous treatment and diagnosis in juvenile dogs presenting with pyrexia to a UK referral centre. MATERIALS AND METHODS: Clinical records of dogs aged 1 to 18 months presenting with a problem list including pyrexia (≥⃒39∙2°C) that was reproducible during referral hospitalisation were retrospectively reviewed. Signalment, history - including previous treatment, clinical examination findings and diagnosis were recorded. Diagnoses were categorised as non-infectious inflammatory, infectious, congenital, neoplastic and miscellaneous. The influence of previous treatment on the ability to reach a final diagnosis was analysed. RESULTS: A total of 140 cases was identified. Diagnosis was reached in 115 cases. Non-infectious inflammatory disease was identified in 91 cases (79%), infectious disease in 19 cases (17%), a congenital disorder in four dogs (3%) and neoplasia in one dog (1%). Breeds most commonly identified were Border collies (17/140; 12%), beagles (16/140; 11%), Labrador retrievers (11/140; 8%), springer spaniels (9/140; 6%) and cocker spaniels (8/140; 6%). Before presentation, most dogs had received antibiotics (83/140; 59%), non-steroidal anti-inflammatory drugs (84/140; 60%) or steroids (9/140; 6%), either alone or in combination. Neither antibiotics nor non-steroidal anti-inflammatory drugs influenced the ability to reach a diagnosis. Steroid-responsive meningitis-arteritis comprised 55 of 91 (60%) individuals of the non-infectious inflammatory cohort. All four dogs diagnosed with congenital disorders were Border collies. CLINICAL SIGNIFICANCE: Non-infectious inflammatory disease, particularly steroid-responsive meningitis-arteritis, immune-mediated polyarthritis and metaphyseal osteopathy, was commonly diagnosed in this population of pyrexic juvenile dogs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".