Getting a grip: cats respond negatively to scruffing and clips
Bibliographic record
Abstract
Use of scruffing and scruffing tools (eg, clipnosis clips) to immobilise cats is contentious, and cat handling guidelines vary in recommendations regarding these techniques. The current study examined whether cats show negative responses to the following restraint methods: (1) scruff (n=17), (2) clip application to the dorsal neck skin (n=16) and (3) full body (a known negative; n=19). Each cat was also handled with passive restraint (control) for comparison. During handling, cats were examined for behavioural (side/back ear positions, vocalisations, lip licking) and physiological (pupil dilation ratio, respiratory rate) responses. Full‐body restrained cats showed more negative responses than passively restrained cats (respiratory rate: p=0.006, F 3,37 =4.31, p=0.01; ear p=0.002, F 3,49 =6.70, p=0.0007; pupil: p=0.007, F 3,95 =14.24, p=0.004; vocalisations: p=0.009, F 3,49 =4.85, p=0.005) and scruff‐restrained cats (pupil: p=0.009; vocalisations: p=0.04). Clip restraint resulted in more negative responses than passive (pupil: p=0.01; vocalisations: p=0.007, ear p=0.02) and scruff restraint (pupil p=0.01; vocalisations: p=0.02). No differences were detected between full‐body restraint, known to be aversive, and clip restraint. Full‐body restraint and clip restraint resulted in the greatest number of negative responses, scruffing resulted in fewer negative responses and passive restraint showed the least number of responses. We therefore recommend against the use of full‐body and clip restraint, and suggest that scruff restraint should be avoided when possible.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".