A new method for tracing the facial nerve trunk using the posterior auricular nerve
Bibliographic record
Abstract
Tracing the facial nerve trunk is an essential action in parotid surgery, because of the implications of injury to the nerve or its branches. More than a few landmarks that may help the surgeon in this task have been proposed (e.g., the posterior belly of the digastric muscle, the tragal pointer, among others), under the assumption that additional access methods improve the surgical technique and reduce the possibility of harmful post-operative consequences. Here we present evidence that the posterior auricular nerve may be used to trace the facial nerve trunk. We dissected 75 cadaveric heminecks, exposed the auricularis posterior muscle and adnexa, and attempted to follow the posterior auricular nerve to the facial nerve trunk. The auricularis posterior muscle, nerve, and artery were identified in all heminecks, securing an anatomically reliable route to the facial nerve trunk. Average length of the nerve from the auricularis posterior muscle to the facial nerve trunk was 28 mm (±6.2 mm). The angle between the posterior auricular nerve and the vertical segment of the FN trunk was 39.5° (±7.7°). We conclude that the posterior auricular nerve may be used as a landmark to trace the facial nerve trunk. It is advantageous due to the relatively simple and consistent regional anatomy, and also because manipulation of this nerve does not present a risk given that the auricularis posterior muscle is vestigial. The proposed landmark is particularly important in revision surgery, where the pre-auricular anatomy may have been distorted and scarred by previous operations. Clin. Anat. 32:453-457, 2019. © 2019 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".