Effects of lidocaine administration via continuous rate infusion on the minimum alveolar concentration of isoflurane in New Zealand White rabbits (Oryctolagus cuniculus)
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of a continuous rate infusion (CRI) of lidocaine on the minimum alveolar concentration (MAC) of isoflurane in rabbits. ANIMALS: Five 12-month-old female New Zealand White rabbits (Oryctolagus cuniculus). PROCEDURES: Rabbits were anesthetized with isoflurane. Baseline isoflurane MAC was determined by use of the tail clamp technique. A loading dose of lidocaine (2.0 mg/kg, IV) was administered followed by a CRI of lidocaine at 50 μg/kg/min. After 30 minutes, isoflurane MAC was determined. Another loading dose was administered, and the lidocaine CRI then was increased to 100 μg/kg/min. After 30 minutes, isoflurane MAC was determined again. Plasma samples were obtained for lidocaine analysis after each MAC determination. RESULTS: Baseline isoflurane MAC was 2.09%, which was similar to previously reported values in this species. Lidocaine CRI at 50 and 100 μg/kg/min induced significant reductions in MAC. The 50 μg/kg/min CRI resulted in a mean plasma lidocaine concentration of 0.654 μg/mL and reduction of MAC by 10.5%. The 100 μg/kg/min CRI of lidocaine resulted in a mean plasma concentration of 1.578 μg/mL and reduction of MAC by 21.7%. Lidocaine also induced significant decreases in arterial blood pressure and heart rate. All cardiopulmonary variables were within reference ranges for rabbits anesthetized with inhalation anesthetics. No adverse effects were detected; all rabbits had an uncomplicated recovery from anesthesia. CONCLUSIONS AND CLINICAL RELEVANCE: Lidocaine administered as a CRI at 50 and 100 μg/kg/min decreased isoflurane MAC in rabbits. The IV administration of lidocaine may be a useful adjunct in anesthesia of rabbits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".