Characterization of pain behaviors in kittens following ovariohysterectomy using video assessment
Bibliographic record
Abstract
This study aimed to characterize the duration and/or frequency of pain behaviors in kittens following ovariohysterectomy using video assessment. A total of 229 videos comprising 18 h of recordings were obtained during a prospective, randomized, clinical trial using an opioid-free protocol with (multimodal group, MMG) or without (control group, CG) multimodal analgesia. Videos included behaviors of 36 kittens (≤ 6 months) before and after surgery, as well as pre/post rescue analgesia. A veterinary behaviorist blinded to treatments and timepoints performed the behavioral assessment using an ethogram. Statistical analyses were performed using linear models (P < 0.05). Duration (%) of 'no attention to surroundings' (5 ± 16 and 0.0 ± 0.7, P = 0.02), 'lowered head position' (4 ± 12 and 0.3 ± 2, P = 0.009) and 'eyes partially closed' (15 ± 29 and 5 ± 17, P < 0.02) was longer in kittens before than after analgesia, respectively. When compared with baseline, kittens in MMG had longer duration of playing (i.e. 'pawing', %) (35 ± 34) than CG (7 ± 12, P = 0.001) at 1 h postoperatively. This study identified behavioral differences between painful and non-painful kittens following ovariohysterectomy contributing to feline acute pain assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".