Acquired Facial, Maxillofacial, and Oral Asymmetries—A Review Highlighting Diagnosis and Management
Bibliographic record
Abstract
Facial asymmetry refers to the absence of, or the deviation from the regular mirror image of facial structures, relative to a referenced midline axis. It can be attributed to a wide spectrum of deformities, including congenital, developmental, or acquired conditions, which can originate either prenatally or postnatally. Though highly prevalent, asymmetry commonly goes undiagnosed due to its subtle or relative nature. Among the spectrum of conditions, acquired cases are triggered postnatally, in previously normal individuals, thus subjecting them to sudden, eventful psychological and psychosocial disharmony. When detected early, timely management may help intervene progressive growth of these conditions. This, therefore, emphasizes the need for a thorough diagnostic workup including medical/dental history, clinical examinations, study models, photographic and radiographic records for a case-by-case basis to prevent severe functional and aesthetic complications. Recently, advanced diagnostic procedures, such as stereophotogrammetry, 3D stereolithographic models, skeletal scintigraphy (radionucleotide scans), 3D computed tomographic scans, cone-beam computed tomography, and magnetic resonance imaging, have provided innovative diagnostic instruments for numerous craniofacial defects. This descriptive review aims at focusing on the factors leading to frequently encountered conditions of acquired facial asymmetry and highlights their clinical evaluation, conservative and surgical interventions by a multi-disciplinary team of clinicians.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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".