Defining the Safe Entry Point in Deep Plane Facelifting with Novel Landmark for the Buccal Branch of the Facial Nerve
Bibliographic record
Abstract
Background: In deep facial surgery, accurate preoperative landmarking of branches of the facial nerve is helpful in avoiding inadvertent facial nerve injury. The objective of our study was to determine the accuracy at which the intersection point of two bisecting lines that join facial surface landmarks can be used to accurately locate the buccal branch(es) of the facial nerve, specifically at the deep plane entry point (ie, intercept landmark). Methods: Thirty-three cadavers were dissected to determine the position of the buccal rami relative to the intercept. Results: Buccal rami crossed the intercept in 12.12% of specimens (0 mm from intercept, n = 4). Buccal rami passed superiorly in 66.67% of specimens (3.71 ± 3.28 mm from intercept, n = 7) and inferiorly in 21.21% of specimens (2.44 ± 0.92 mm from intercept, n = 7). Noteworthy, buccal rami were located within 1 cm of the intercept landmark with 96.97% accuracy (32/33 cadavers). Conclusions: These data suggest that this novel intercept (1) reliably locates the buccal branch of the facial nerve as it courses distal to the parotid gland, and (2) helps define a "safe zone" for entry into the deep plane where the likelihood of encountering the facial nerve is extremely low.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".