Evaluation of clinically relevant landmarks of the marginal mandibular branch of the facial nerve: A three‐dimensional study with application to avoiding facial nerve palsy
Bibliographic record
Abstract
Injury to the marginal mandibular branch of the facial nerve (MMN) during surgery often results in poor functional and cosmetic outcomes. A line two finger breadths or 2 cm inferior to the border of the mandible is commonly used in planning neck incisions to avoid injury to the MMN. The purpose was to compare the two finger breadth/2 cm landmarks in predicting MMN course, and their accuracy/reliability. Thirty-one cadaveric specimens were scanned to obtain 3D surface topography (FARO® scanner). Four independent raters pinned the inferior border of the mandible and a two finger breadth line and 2cm line below. The location of each pin was digitized (Microscribe™). A preauricular flap was raised, and MMN branches were digitized and modelled (Geomagic®/Maya®) enabling quantification of the accuracy of these landmarks. The location of the two-finger breadth line was variable, spanning 25-51 mm below the inferior border of the mandible (ICC = 0.10). The most inferior MMN branch did not pass below the two-finger breadth line in any specimen, but a narrow clearance zone (≤5 mm) was found in two. In contrast, in 7/31 specimens, the most inferior MMN branch coursed below the 2 cm line and would be at risk of injury. It was concluded that an incision two finger breadths below the inferior border of the mandible could provide safer access than the 2 cm line. After an incision has been placed using the two finger-breadth landmark, caution must be exercised during dissection as branches of the MMN may lie only a few millimeters superior to the incision.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".