A Novel Technique for Malar Eminence Evaluation Using 3-Dimensional Computed Tomography
Bibliographic record
Abstract
To describe a novel method to locate the malar eminence using 3-dimensional computed tomography (3D-CT), and a new axis system for evaluation of malar eminence symmetry. A retrospective case series was carried out in 42 disease-free white adult patients. The 3D-CT reconstructions of the face were obtained, and the soft-tissue maxillozygion was used to locate the malar eminence. Other skeletal and soft-tissue landmarks (frontozygomatic suture, zygion, and orbitale) were evaluated. A patient-oriented axis system was constructed using 3 sagittal midline landmarks (nasion, subspinale, and basion). Coordinates were obtained for each landmark, and symmetry was evaluated. Twenty-one men and 21 women with mean ages of 41.1 and 41.3 years, respectively, were included. The malar eminence was easily localized using the 3D-CT technique for soft-tissue maxillozygion identification. Clinical asymmetry at the level of the soft-tissue maxillozygion was 40.5% (95% CI, 25.0%-56.0%). Other landmarks showed a prevalence of clinical asymmetry ranging from 24.0% to 50.0%. The malar eminence can be easily and precisely located using the 3D-CT soft-tissue maxillozygion landmark. A reliable patient-oriented axis system can be defined using nasion, subspinale, and basion. The prevalence of malar eminence asymmetry in our study was 40.5%. Moubayed and coauthors describe a novel method to locate the malar eminence using 3D-CT and a new axis system for evaluation of malar eminence symmetry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".