Pain sensitivity differs between dog breeds but not in the way veterinarians believe
Bibliographic record
Abstract
Background: Veterinarians hold distinct breed-specific pain sensitivity beliefs that differ from the general public but are highly consistent with one another. This is remarkable as there is no current scientific evidence for biological differences in pain sensitivity across dog breeds. Therefore, the present study evaluated whether pain sensitivity thresholds differ across a set of dog breeds and, if so, whether veterinarians' pain sensitivity ratings explain these differences or whether these ratings are attributed to behavioral characteristics. Methods: Pain sensitivity thresholds [using quantitative sensory testing (QST) methods] and canine behaviors (using owner questionnaires and emotional reactivity tests) were prospectively measured across selected dog breeds. Adult, healthy dogs from 10 dog breeds/breed types were recruited, representing breeds subjectively rated by veterinarians as high (chihuahua, German shepherd, Maltese, Siberian husky), average (border collie, Boston terrier, Jack Russell terrier), or low (golden retriever, pitbull, Labrador retriever) pain sensitivity. A final sample of 149 dogs was included in statistical analyses. Results: Veterinarians' pain sensitivity ratings provided a minimal explanation for pain sensitivity thresholds measured using QST in dogs; however, dog breeds did differ in their pain sensitivity thresholds across the QST methods evaluated. Breed differences were observed for some aspects of emotional reactivity tests; however, these behavioral differences did not explain the differences in pain sensitivity thresholds found. Veterinarians' pain sensitivity ratings were positively associated with dog approach scores for the disgruntled stranger test suggesting that the way dogs greet strangers may be a factor influencing veterinarians' ratings of pain sensitivity across dog breeds. Conclusions and clinical relevance: Overall, these findings highlight a need to investigate biological mechanisms that may explain breed differences in pain sensitivity because this may inform pain management recommendations. Further, future research should focus on when and how these breed-specific pain sensitivity beliefs developed in veterinarians, as veterinarians' beliefs could impact the recognition and treatment of pain for canine patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".