Border Collie Collapse: Owner Survey Results and Veterinary Description of Videotaped Episodes
Bibliographic record
Abstract
Completed surveys were obtained from owners of 165 border collies experiencing repeated episodes of abnormal gait or collapse during strenuous exercise. Unremarkable veterinary evaluation and lack of disease progression over time made common systemic, cardiac, and neurologic causes of exercise intolerance unlikely. Survey questions addressed signalment, age of onset, description of episodes, and owner perception of factors associated with collapse. Most dogs were young adults (median 2 yr) when episodes began, and they had experienced from 2 to more than 100 episodes (median 6) prior to their owners completing the survey. Retrieving was the activity most commonly associated with episodes (112/165 dogs, 68%), followed by herding stock (39/165 dogs, 24%). Owners reported that high environmental temperatures (111/165 dogs, 67%) and excitement (67/165 dogs, 41%) increased the likelihood of their dog having an episode during strenuous activity. Veterinary evaluation of videotapes of presumed border collie collapse (BCC) episodes (40 dogs) were used to provide a description of the typical features of BCC episodes. Altered mentation, symmetrical ataxia affecting all four limbs, increased pelvic limb extensor tone and toe scuffing or knuckling, truncal swaying, and falling to the side were common features, suggesting that BCC may be an episodic diffuse central nervous system disorder.
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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 | 0.001 | 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) | 0.001 | 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".