Three-Dimensional Assessment of Temporomandibular Joint Morphology and Facial Asymmetry in Individuals with Different Vertical Skeletal Growth Patterns
Bibliographic record
Abstract
The aim of the current study was to investigate, by means of Cone-Beam Computed Tomography (CBCT), condyle−fossa relationship, temporomandibular joint (TMJ) morphology and facial asymmetry in subjects with different vertical skeletal growth patterns. CBCT of 56 patients (112 TMJs) were categorized into three groups according to the mandibular plane angle (MP): Hypodivergent (MP ≤ 23°), Normodivergent (23° < MP < 30°), and Hyperdivergent (MP ≥ 30°). TMJ spaces, width and depth of the condyle and thickness of the fossa were measured. Horizontal and vertical measurements were used to assess facial asymmetry. One-way Analysis of Variance (ANOVA) and post-hoc Turkey tests were computed for the between-groups comparison. Statistical significance was set at p < 0.05. Larger anterior joint space and smaller condylar dimensions (medio-lateral diameter and medio-lateral thickness) were observed in the hyperdivergent group compared to the normodivergent and hypodivergent groups. Right condylar distances to midsagittal plane were significantly larger than left distances in all the three groups. A vertical pattern of growth in healthy individuals seems to be associated with condylar position and dimension, while facial asymmetry values do not differ among different vertical groups.
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 | 0.000 |
| 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) | 0.001 | 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".