A survey of risk factors for digit injuries among dogs training and competing in agility events
Bibliographic record
Abstract
OBJECTIVE To identify potential risk factors for digit injuries in dogs training and competing in agility events. DESIGN Internet-based, retrospective, cross-sectional survey. ANIMALS 1,081 dogs training or competing in agility events. PROCEDURES Data were collected for eligible animals via retrospective surveys distributed electronically to handlers of dogs participating in agility-related activities. Variables evaluated included demographic (handlers) and signalment (dogs) information, physical characteristics of dogs, and injury characteristics. A separate survey of dogs competing in similar agility-related activities but without digit injuries was also administered. Multivariable logistic regression was used to develop a model for assessment of risk factors. RESULTS Data were collected from 207 agility dogs with digit injuries and 874 agility dogs without digit injuries. Factors associated with significantly increased odds of injury included Border Collie breed (OR, 2.3; 95% confidence interval [CI], 1.5 to 3.3), long nails (OR, 2.4; 95% CI, 1.3 to 4.5), absence of front dewclaws (OR, 1.9; 95% CI, 1.3 to 2.6), and greater weight-to-height ratio (OR, 1.5; 95% CI, 1.1 to 2.0). Odds of injury decreased with increasing age of the dog (OR, 0.8; 95% CI, 0.76 to 0.86). CONCLUSIONS AND CLINICAL RELEVANCE Results should be cautiously interpreted because of potential respondent and recall bias and lack of review of medical records. Nevertheless, results suggested that retaining healthy dewclaws, maintaining lean body mass, and trimming nails short for training and competition may decrease the likelihood of digit injuries. Research to investigate training practices, obstacle construction specifcations, and surface considerations for dogs competing in agility activities is indicated.
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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".