Lidocaine Infusion Improves the Functionality of Intraoperative Nerve Monitoring During Thyroid Surgery: A Prospective, Randomized, Double-Blinded Study
Bibliographic record
Abstract
Background: Intraoperative nerve monitoring (IONM) to assess the recurrent laryngeal nerve function during thyroid surgery is becoming the standard of care across many institutions. The successful deployment and data analysis from the IONM require complete laryngeal relaxation and reflex suppression. We investigated the role of intravenous lidocaine infusion (IVLI) to provide such operating conditions, under a lighter plane of anesthesia and fewer hemodynamic fluctuations. Methods: Sixty-five patients were randomly assigned to lidocaine group (LG) or placebo group (PG) based on the computer-generated coding developed by the pharmacy department. The study medication (SM) was delivered by the pharmacist in a pre-filled coded syringe to the investigator 30 min prior to the surgery. All the patients were anesthetized by narcotic and inhalation based general anesthesia. The SM was administered at the rate of 1.5mg/kg/h following a loading dose of 1 mg/kg. Dragonfly ® laryngeal surface electrode and Nerveana ® nerve locator system were used for IONM during surgery. Results: The proportion of patients requiring lower strength stimulating current (StMC) at 0.5 mA was significantly higher in the LG than in the PG (X 2 (1, N = 61) = 10.1615, P = 0.001434). Similarly, the proportion of patients with the drop in the aggregate impedance level (DAIL) by < 50% at the end of surgery was significantly higher in the LG than in the PG (X 2 (1, N = 61) = 15.982, P = 0.000064). In addition, the proportion of patients with the hypotensive episodes requiring rescue medications more than twice during surgery was significantly lower in the LG than in the PG (X 2 (1, N = 61) = 0.0183, P < 0.05). Conclusions: The enhanced laryngeal relaxation and the reflex suppression afforded by the IVLI could have enabled a lower StMC to elicit a positive signal. The lower StMC promotes less intense laryngeal alterations as evidenced by the lower DAIL in the LG. IVLI can enhance the functionality of the IONM during prolonged operating time and the resultant increased number of IONM stimulations, while providing a stable hemodynamic environment. J Clin Med Res. 2021;13(4):214-221 doi: https://doi.org/10.14740/jocmr4458
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".