Application of a Novel Semi-Automatic Technique for Determining the Bilateral Symmetry Plane of the Facial Skeleton of Normal Adult Males
Bibliographic record
Abstract
The accurate assessment of symmetry in the craniofacial skeleton is important for cosmetic and reconstructive craniofacial surgery. Although there have been several published attempts to develop an accurate system for determining the correct plane of symmetry, all are inaccurate and time consuming. Here, the authors applied a novel semi-automatic method for the calculation of craniofacial symmetry, based on principal component analysis and iterative corrective point computation, to a large sample of normal adult male facial computerized tomography scans obtained clinically (n = 32). The authors hypothesized that this method would generate planes of symmetry that would result in less error when one side of the face was compared to the other than a symmetry plane generated using a plane defined by cephalometric landmarks. When a three-dimensional model of one side of the face was reflected across the semi-automatic plane of symmetry there was less error than when reflected across the cephalometric plane. The semi-automatic plane was also more accurate when the locations of bilateral cephalometric landmarks (eg, frontozygomatic sutures) were compared across the face. The authors conclude that this method allows for accurate and fast measurements of craniofacial symmetry. This has important implications for studying the development of the facial skeleton, and clinical application for reconstruction.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".