Clinical use of the parasympathetic tone activity index as a measurement of postoperative analgaesia in dogs undergoing ovariohysterectomy
Bibliographic record
Abstract
Abstract Introduction While the current tools to assess canine postoperative pain using physiological and behavioural parameters are reliable, an objective method such as the parasympathetic tone activity (PTA) index could improve postoperative care. The aim of the study was to determine the utility of the PTA index in assessing postoperative analgaesia. Material and Methods Thirty healthy bitches of different breeds were randomly allocated into three groups for analgaesic treatment: the paracetamol group (G PARAC , n = 10) received 15 mg/kg b.w., the carprofen group (G CARP , n = 10) 4 mg/kg b.w., and the meloxicam group (G MELOX , n = 10) 0.2 mg/kg b.w. for 48 h after surgery. G PARAC was medicated orally every 8 h, while G CARP and G MELOX were medicated intravenously every 24 h. The PTA index was used to measure the analgaesia–nociception balance 1 h before surgery (baseline), and at 1, 2, 4, 6, 8, 12, 16, 20, 24, 36, and 48 h after, at which times evaluation on the University of Melbourne Pain Scale (UMPS) was made. Results The baseline PTA index was 65 ± 8 for G PARAC , 65 ± 7 for G CARP , and 62 ± 5 for G MELOX . Postoperatively, it was 65 ± 9 for G PARAC , 63 ± 8 for G CARP , and 65 ± 8 for G MELOX . No statistically significant difference existed between baseline values or between values directly after treatments (P = 0.99 and P = 0.97, respectively). The PTA index showed a sensitivity of 40%, specificity of 98.46% and a negative predictive value of 99.07%. Conclusion Our findings suggest that the PTA index measures comfort and postoperative analgaesia objectively, since it showed a clinical relationship with the UMPS.
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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 | 0.001 | 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 | 0.000 |
| 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".