Improving the Functionality of Intra-Operative Nerve Monitoring During Thyroid Surgery: Is Lidocaine an Option?
Bibliographic record
Abstract
Intra-operative nerve monitoring (IONM) is rapidly becoming a standard of care in many institutions across the country. In the absence of neuromuscular blocking agents to facilitate the IONM, the depth of anesthesia required to abolish the laryngo tracheal reflexes often results in profound hemodynamic instability during surgery, necessitating the use of large doses of sympathomimetic amines. The excessive alpha and beta adrenergic effects exhibited by these agents are undesirable in the presence of cardiovascular co-morbidities. Trying to strike a balance frequently results in an unsatisfactory intra-operative course. In the course of the near total thyroidectomy performed on a 60-year-old female, we employed lidocaine infusion at 1.5 mg/kg/hour following a bolus dose of 1 mg/kg. The troublesome laryngo tracheal reflexes were successfully blunted and we were able to moderate the depth of anesthesia resulting in stable hemodynamics. A bispectral index monitor was employed to guard against "recall" and a train of four monitor was used to ensure the absence of inadvertent neuromuscular blockade. During the surgery, there was loss of signal on the left recurrent laryngeal nerve (RLN). The signal strength was restored by rotating the endotracheal tube on its long axis to realign the electrode with the vocal cords under Glidescope(®) visualization.
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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 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".