Clinical and Breed Characteristics of Idiopathic Head Tremor Syndrome in 291 Dogs: A Retrospective Study
Bibliographic record
Abstract
Objective. To establish signalment and phenomenology of canine idiopathic head tremor syndrome (IHTS), an episodic head movement disorder of undetermined pathogenesis. Design. Retrospective case series. Animals. 291 dogs with IHTS diagnosed between 1999 and 2013. Procedures. Clinical information was obtained from an online community of veterinary information aggregation and exchange (Veterinary Information Network, 777 W Covell Boulevard, Davis, CA 95616) and conducted with their approval. Information on breed, sex, age of onset, tremor description, mentation during the event, effect of distractions and drugs, diagnostics, presence of other problems, and outcome was analyzed. Results. IHTS was found in 24 pure breeds. Bulldogs, Labrador Retrievers, Boxers, and Doberman Pinschers comprised 69%; mixed breeds comprised 17%. Average onset age was 29 months (range: 3 months to 12 years). First episode occurred before 48 months of age in 88%. Vertical (35%), horizontal (50%), and rotational (15%) movements were documented. Possible trigger events were found in 21%. Mentation was normal in 93%. Distractions abated the tremor in 87%. Most dogs did not respond to antiepileptic drugs. Conclusions and Clinical Relevance. This retrospective study documents IHTS in many breeds including Labrador Retrievers, Boxers, and mixed breeds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".