Assessing the affective component of pain, and the efficacy of pain control, using conditioned place aversion in calves
Bibliographic record
Abstract
Pain in animals is typically assessed using reflexive and physiological responses. These measures allow inferences regarding nociception but provide little basis for conclusions about the affective component of pain (i.e. how negatively the experience is perceived). Calves routinely undergo painful procedures on commercial farms, including hot-iron disbudding, providing a convenient model to study pain in animals. The aim of this study was to investigate the affective component of post-procedural pain due to hot-iron disbudding, using conditioned place aversion. Calves (n= 31) were subjected to two procedures (one bud at a time): one without post-procedural pain control and the other with the use of a nonsteroidal anti-inflammatory drug (either meloxicam (n= 16) or ketoprofen (n= 15)). All procedures included the use of local anaesthesia (lidocaine). Place conditioning was tested 2 days after the last treatment by allowing calves to freely roam between the pens where they had previously been disbudded. Calves spent more time, and lay down more frequently, in the pen where they received meloxicam compared with the pen where they only received a local block. Surprisingly, calves avoided the pen where they received ketoprofen compared with the control treatment pen. We hypothesize that the shorter duration of action of ketoprofen resulted in increasing pain at the end of the conditioning period, explaining the increased aversion to this treatment. These results illustrate the value of place conditioning paradigms to assess the affective component of pain in animals, and suggest that the animal's evaluation of painful events depends upon the time course of when the pain is experienced.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".