Survey-based analysis of risk factors for injury among dogs participating in agility training and competition events
Bibliographic record
Abstract
OBJECTIVE: To identify potential risk factors for agility-related injuries among dogs. DESIGN: Internet-based, retrospective, cross-sectional survey. ANIMALS: 3,801 privately owned dogs participating in agility training or trials. PROCEDURES: A retrospective electronic survey was used to investigate potential risk factors for injury among dogs participating in agility-related activities. Respondents were handlers recruited through member lists of large canine agility associations in Canada and the United Kingdom and through promotion on an agility blog site. Variables evaluated included demographic information for handlers and dogs, exposure variables (eg, frequency of agility practice and competition in the past year), and use of preventive measures intended to keep dogs fit for agility (warmup, cooldown, or conditioning exercises; alternative therapeutic treatments [eg, acupuncture, massage, or chiropractic care]; or dietary supplement products). RESULTS: Data were collected from 1,669 handlers of 3,801 agility dogs internationally; 1,209 (32%) dogs incurred ≥ 1 injury. Previous injury (OR, 100.5), ≤ 4 years of agility experience for dogs (OR, 1.5), use of alternative therapeutic treatments (OR, 1.5), and Border Collie breed (OR, 1.7) were associated with increased odds of injury. Handlers having 5 to 10 or > 10 years of experience (OR, 0.8 and 0.6, respectively) and dogs having > 4 years of experience in the sport (OR, 0.6) were associated with decreased odds of injury. CONCLUSIONS AND CLINICAL RELEVANCE: Specific factors were associated with agility-related injuries in dogs. Educational prevention strategies should target at-risk populations in an effort to reduce potential injuries. Future research should focus on the biomechanical factors associated with agility-related injuries.
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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 | 0.005 |
| 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".