Equipotent Dose and Cost Comparison of Atracurium and Rocuronium in Laboratory Pigs Anesthetized with Propofol
Bibliographic record
Abstract
Neuromuscular blocking agents such as atracurium and rocuronium are commonly used during anesthetic procedures in laboratory pigs. However, species-specific dosing guidelines remain limited, leading to reliance on data extrapolated from other species. This prospective, blinded study aimed to determine the equipotent dose for atracurium (A) and rocuronium (R) in laboratory pigs receiving propofol and to compare their cost-effectiveness. Twelve healthy animals were randomly distributed according to the drug administered (n = 6 per group). For both drugs, the infusion rate was adjusted following an up-and-down titration to maintain a train-of-four count between 3 and 4. Group differences were analyzed using the Wilcoxon signed-rank test. The bolus induction dose (mg/kg) was comparable between atracurium (2.3 [1.8–2.6]) and rocuronium (2 [2]), while atracurium was associated with higher costs (CHF/kg: A, 1.122 [0.878–1.366] versus R, 0.208 [0.208–0.208]; p = 0.002725). The maintenance infusion rate (mg/kg/h) was approximately 40% lower for atracurium (2.7 [2.5–2.8]) than for rocuronium (4.5 [4.4–4.5]; p = 0.004922), yet the maintenance cost (CFH/kg/h) remained higher for atracurium (A: 1.30 [1.22–1.37] versus R: 0.47 [0.45–0.47]; p = 0.0043). This study reports higher doses for anesthetized pigs compared to other species and demonstrates that rocuronium offers superior cost-effectiveness compared to atracurium under these experimental conditions.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".