Effect of pretreatment with hydromorphone or buprenorphine on thermal antinociception induced by fentanyl in awake cats
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to determine the effect of pretreatment with hydromorphone or buprenorphine on thermal antinociception induced by fentanyl in cats. METHODS: Seven healthy cats received five different treatments consisting of two drugs. Drug 1 was administered intravenously 1 h before drug 2. Drug 2 was given as an intravenous loading dose followed by an infusion for 4 h. The drug combinations used were: buprenorphine 20 µg/kg followed by fentanyl (BF; 7 μg/kg, 7 μg/kg/h), buprenorphine 20 µg/kg followed by 0.9% saline solution (B), hydromorphone 0.07 mg/kg followed by 0.9% saline solution (H), hydromorphone 0.07 mg/kg followed by fentanyl (HF; 7 μg/kg, 7 μg/kg/h) and 0.9% saline solution followed by fentanyl (F; 7 μg/kg, 7 μg/kg/h). Thermal thresholds were obtained prior to treatment and at predetermined times up to 7 h after drug 1. RESULTS: Thermal thresholds were significantly higher than baseline in all treatment groups as follows: H from 0.25 to 2.50 h; B from 0.25 to 2.50 h; F from 1.25 to 5.50 h; HF from 0.25 to 5.50 h and BF from 0.25 to 5.25 h. Thermal thresholds were significantly higher in HF and BF than in F treatment before the fentanyl infusion was commenced (from 0.25 to 1.00 h). During the fentanyl infusion thermal thresholds in BF compared with F were lower at 1.75 h and from 2.50 to 3.50 h. After the constant rate infusion of fentanyl was started, thermal thresholds were significantly lower in HF compared with F at one time point (3 h). CONCLUSIONS AND RELEVANCE: Pretreatment with buprenorphine did partially inhibit the antinociceptive action of fentanyl. Hydromorphone did inhibit the antinociceptive action of fentanyl at one single time point in cats.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".