Effect of a constant rate infusion of lidocaine on the quality of recovery from sevoflurane or isoflurane general anaesthesia in horses
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Lidocaine constant rate infusions (CRIs) are common as an intraoperative adjunct to general anaesthesia, but their influence on quality of recovery has not been thoroughly determined. OBJECTIVES: To determine the effects of an intraoperative i.v. CRI of lidocaine on the quality of recovery from isoflurane or sevoflurane anaesthesia in horses undergoing various surgical procedures, using a modified recovery score system. HYPOTHESIS: The administration of intraoperative lidocaine CRI decreases the quality of recovery in horses. METHODS: Lidocaine (2 mg/kg bwt bolus followed by 50 microg/kg bwt/min) or saline was administered for the duration of surgery or until 30 mins before the end of surgery under isoflurane (n = 27) and sevoflurane (n = 27). RESULTS: Horses receiving lidocaine until the end of surgery had a significantly higher degree of ataxia and a tendency towards significance for a lower quality of recovery. There was no correlation between lidocaine plasma concentrations at recovery and the quality of recovery. CONCLUSIONS: Intraoperative CRI of lidocaine affects the degree of ataxia and may decrease the quality of recovery. POTENTIAL RELEVANCE: Discontinuing lidocaine CRI 30 mins before the end of surgery is recommended to reduce ataxia during the recovery period.
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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) | 0.001 | 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".