Clinically relevant landmarks of the frontotemporal branch of the facial nerve: A three‐dimensional study
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Bibliographic record
Abstract
The frontotemporal branch of the facial nerve (FTN) is vulnerable during craniofacial surgeries due to its superficial course and variable distribution. Surface landmarks that correlate with the underlying course of the FTN can assist in surgical planning. Estimates of the course of FTN commonly rely on Pitanguy's line (PL), which utilizes variable soft-tissue landmarks. The purpose of this study was to evaluate palpable surface landmarks to predict the course and distribution of FTN using 3D modeling. Fifteen half-heads were used. In five formalin-embalmed specimens, surface topography was obtained using a FARO® scanner and landmarks corresponding to PL, porion, supraorbital notch, frontozygomatic and zygomaticotemporal sutures, and supraorbitomeatal line (SOML) and infraorbitomeatal line (IOML) were demarcated/digitized using a Microscribe™ digitizer. A preauricular flap was raised, and branches of FTN were isolated and digitized. The data were reconstructed into 3D models (Geomagic®/Maya®) to quantify landmarks. In 10 Thiel-embalmed specimens, four independent raters identified/palpated and pinned the frontozygomatic and zygomaticotemporal sutures and PL. Data were collected and analyzed using the same protocol as in the first part of the study. Landmarking of PL was inconsistent between raters and not representative of FTN distribution. The easily identifiable surface landmarks defined in this study, a line 12 mm anterior to the porion along the SOML and IOML and a line joining the zygomaticotemporal and frontozygomatic sutures, comprehensively captured the distribution of FTN. The raters found a mean of 21 ± 2 branches between the lines out of a total of 22 ± 2 branches. These landmarks may be used clinically to avoid injury to FTN.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it