Anesthetic Effects and Lipid Resuscitation Protocols
Bibliographic record
Abstract
We thank Drs. Woehlck and El-Orbany for their interest in our recently published article that examined lipid emulsion using a porcine model of bupivacaine-induced cardiac arrest.1Their letter raises several important issues. In the Discussion section of our article, we explained some of the major differences between the various animal models that have been used to evaluate lipid treatment for local anesthetic toxicity.We recognize the potential drug interactions between the anesthetics agents and the experimental protocol. The anesthetic agents, such as xylazine, ketamine, and α-chloralose, were chosen to preserve hemodynamic and electrophysiologic stability at the doses used in our study. Propofol was avoided because of the confounding effect of lipid pretreatment, as found in other animal studies of this nature. Despite this limitation, we were able to achieve a stable hemodynamic profile in all animals before the induction of cardiac arrest with the bupivacaine injection. After examining the electrocardiographic data (mean ± SD), we did not observe any occurrences of prolonged PR (120.6 ± 13.7), QRS (59.1 ± 14.6), or QTc intervals (347.4 ± 26.4), during the baseline period before the induction of cardiac arrest.2Our electrophysiologic data are perhaps different from the results mentioned in the letter of Woehlck and El-Orbany because much higher doses of α-chloralose were used in other studies (75 mg/kg in one study3and 100 mg/kg in another4) in contrast to a moderate dose of 40 mg/kg used in our study.We agree with Drs. Woehlck and El-Orbany that it would be useful to consider further experiments that expand our understanding of the potential therapeutic benefits of lipid emulsion in the setting of cardiac arrest induced by toxic doses of local anesthetic, especially at a time when various national and international organizations are in the process of developing recommendations incorporating lipid treatment.*University of Ottawa, Ottawa, Ontario, Canada, and University of Pittsburgh, Pittsburgh, Pennsylvania. shawn_hicks@alumni.pitt.edu
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".