Comparison of inhaled versus intravenous anesthesia for laryngoscopy and laryngeal electromyography in a rat model
Bibliographic record
Abstract
BACKGROUND: Propofol and remifentanil intravenous combination is one popular form of total intravenous anesthesia (TIVA) in mainstream clinical practice, but it has rarely been applied to a rat model for laryngoscopy and laryngeal electromyography (LEMG). Our objective was to establish a safe and reproducible general anesthetic protocol for laryngoscopy and endoscopic LEMG in a rat model. Our hypothesis is that TIVA allows a minimally morbid, and feasible laryngoscopy and LEMG. METHODS: Sprague Dawley rats were subjected to either inhalational anesthesia (IA) (isoflurane) or TIVA (propofol and remifentanil) and underwent laryngoscopy and LEMG. The primary outcome was a complete minimally interrupted rigid laryngoscopy and obtaining reproducible motor unit potentials from the posterior cricoarytenoid muscles. The secondary outcome was morbidity and mortality. RESULTS: Seventeen out of twenty-two rats underwent both TIVA and IA. Only two underwent IA only. All nineteen rats that underwent IA had a successful experiment. Seventeen rats underwent TIVA, however, only nine completed a successful experiment due to difficulty achieving a surgical plane, and respiratory events. Upon comparing the success of the two anaesthetic regimens, IA was superior to TIVA (P = 0.0008). There was no statistical difference between the amplitudes (p = 0.1985) or motor units burst duration (p = 0.82605) of both methods. Three mortalities were encountered, one of which was due to lidocaine toxicity and two were during anesthetic induction. Respiratory related morbidity was encountered in two rats, all seen with TIVA. CONCLUSIONS: TIVA is not an ideal anesthetic regimen for laryngeal endoscopy and LEMG in rat models. Contrary to our hypothesis, IA did not affect the quality of the LEMG and allowed a seamless rigid endoscopy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".