Predicting the course of the marginal mandibular nerve: an evaluation of surgical landmarks
Bibliographic record
Abstract
The inferior branch of the marginal mandibular nerve (IMB) is at risk of injury during surgical neck dissections due to its variability. Often surgeons rely on a two‐finger breadth, and/or 2cm distance below the inferior border of the mandible for submandibular incision placement (Witt, 2006). The purpose was to evaluate the accuracy of the landmarks and to define other fixed landmarks to avoid IMB injury. In ten Thiel embalmed specimens, six independent raters palpated/pinned the inferior border of the mandible and a line two‐finger breadths below. Each pinhead was digitized (Microscribe®) and a superficial musculoaponeurotic (SMAS) flap was raised using a Ridson approach. The IMB was identified and digitized from the parotid gland to the depressor anguli oris/labii inferioris. The digitized data were modeled (Autodesk Maya®) and distances between the IMB and landmarks quantified. The distance from the IMB to the inferior border of the mandible varied from 2.5–11.5mm (7.5±2.9) suggesting that in some cases the 2cm landmark may be risky. The two‐finger breadth line to the IMB was 18.9–38.2mm (30.6±7.2) indicating adequate clearance for the nerve. Other landmarks have been documented. To avoid injury of the IMB during surgical neck dissections, easily measurable and reliable bony landmarks should be identified which can accurately predict the course and distribution of the nerve.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".