Pharmacokinetics and pharmacodynamics of a constant rate infusion of fentanyl (5 μg/kg/h) in awake cats
Bibliographic record
Abstract
OBJECTIVE: To evaluate the pharmacokinetics and thermal and mechanical antinociceptive effects of a fentanyl constant rate infusion (CRI) in conscious cats. ANIMALS: 8 healthy adult cats. PROCEDURES: At a ≥ 14-day interval, 7 cats received a loading dose (LD) of fentanyl (5 μg/kg, IV [administered at 0 hours]) followed by fentanyl infusion (5 μg/kg/h, IV) for 2 hours or similar administrations of equivalent volumes of 0.9% saline (NaCl) solution. One cat received only the fentanyl treatment. For both treatments, sedation and adverse events were evaluated and mechanical threshold (MT) and thermal threshold (TT) testing was performed prior to (baseline) and at predetermined times up to 26 hours after LD administration; plasma fentanyl concentrations were determined at similar times when the cats received fentanyl. RESULTS: Fentanyl induced mild sedation during the infusion. The only adverse effect associated with fentanyl LD administration was profuse salivation (1 cat). Saline solution administration did not significantly change MT or TT over time. For the duration of the CRI, MT and TT differed significantly between treatments, except for TT 1 hour after LD administration. For the fentanyl treatment, MT and TT were significantly higher than baseline at 0.25 to 0.75 hours and at 0.25 to 1 hour, respectively. During the fentanyl CRI, mean ± SD plasma fentanyl concentration decreased from 4.41 ± 1.86 ng/mL to 2.99 ± 1.28 ng/mL and was correlated with antinociception; plasma concentrations < 1.33 ± 0.30 ng/mL were not associated with antinociception. CONCLUSIONS AND CLINICAL RELEVANCE: Fentanyl CRI (5 μg/kg/h) induced mechanical and thermal antinociception 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".