<i>In-Vivo</i> Detection of the Facial Nerve From Adjacent Tissues Using Microelectrodes With Selective Passivation During Parotidectomy
Bibliographic record
Abstract
Iatrogenic facial nerve injury often occurs during parotidectomy owing to the difficulty associated with accurately locating the facial nerve from adjacent tissues (parotid gland and muscle). This injury causes postoperative facial nerve dysfunction, leading to functional and/or cosmetic problems. In this study, we propose an <i>in-vivo</i> detection method to distinguish the facial nerve from the adjacent tissues using electrochemical impedance spectroscopy (EIS). The electrical impedance of the facial nerve, parotid gland, and muscle via <i>in-vivo</i> experiments using guinea pigs (n = 3) was measured using microelectrodes on polyimide film (MoP), consisting of detection and connection parts. The connection part was selectively coated with a photoresist that exhibits high resistivity to attenuate a distortion of the sensor output. Statistically significant differences in impedance of the real and imaginary parts were observed between the facial nerve and the adjacent tissues in the frequency range of 0.99 kHz to 1 MHz (p-value < 0.05, t-test). Optimal frequency, where the difference between adjacent tissues and facial nerve had the most distinction, were 13.64 kHz in the imaginary part of impedance. Additionally, the estimated electrical properties (conductivity and permittivity) of the facial nerve and adjacent tissues were statistically distinguishable (p-value < 0.05, t-test). These findings indicate that MoP with EIS could enable surgeons to easily locate the facial nerve during parotidectomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".