Clinically relevant landmarks of the frontotemporal branch of the facial nerve: A three‐dimensional study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".