Video-based compilation of acute pain behaviours in cats
Bibliographic record
Abstract
OBJECTIVES: The aim of this work was to create a video-based compilation of acute pain behaviours in cats as an open-access online resource for training of veterinary health professionals. METHODS: A database comprising 60 h of video recordings of cats was used. Videos were previously recorded after ethical approval and written client consent forms, and involved cats with different types (eg, medical, surgical, trauma, orofacial) and degrees (eg, from no pain to severe pain) of acute pain, before and after surgery or the administration of analgesia. The database included videos of cats of different coat colours, ages, sex and breeds. Video selection was based on a published ethogram of acute pain behaviours in cats. Videos were selected by one observer (SM) according to their definition and quality, followed by a second round of screening by two observers (SM and PVS). Video editing included a standardised template (ie, watermark and titles). RESULTS: A total of 24 videos (mean length 33 ± 17 s) with each acute pain-related behaviour described in the ethogram were uploaded to an open-access online video-sharing platform (http://www.youtube.com/@Steagalllaboratory) with an individual hyperlink. Videos were provided with a short description of the behaviour for the public. CONCLUSIONS AND RELEVANCE: This video-based compilation may promote better training of veterinary health professionals on acute pain assessment while improving feline health and welfare and the understanding of cat behaviours.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".