Concurrent idiopathic generalised tremor syndrome and bilateral trigeminal neuropathy in a dog ( <i>Canis lupus familiaris</i> )
Bibliographic record
Abstract
Abstract A 3‐year‐old, neutered, male Labrador Retriever presented with a 1‐month history of bilateral temporal muscle atrophy, spontaneously resolving dropped jaw, and progressive generalised tremors. Neurological examination revealed opsoclonus, bilaterally incomplete menace response, increased muscle tone, subtle ambulatory tetraparesis, and reduced pelvic limb postural reactions. A multifocal neuro‐anatomical localisation involving the cerebellum, bilateral trigeminal nerves, C1–C5 and T3–L3 spinal cord segments was suspected. Magnetic resonance imaging showed mild bilateral trigeminal nerve thickening, cerebrospinal fluid analysis was normal, and screening for Neospora caninum and Toxoplasma gondii was negative. A 6‐month tapering course of prednisolone led to remission. Idiopathic generalised tremor syndrome was diagnosed based on characteristic clinical signs, negative diagnostic findings, and response to corticosteroids. The dropped jaw was attributed to idiopathic trigeminal neuropathy, supported by magnetic resonance imaging findings and spontaneous resolution. Generalised tremors recurred 13 months later, but resolved with the same treatment. This case describes a unique presentation of simultaneously occurring idiopathic generalised tremor syndrome and idiopathic trigeminal neuropathy.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".