The relationship between vertical facial morphology and overjet in untreated Class II subjects
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between vertical facial morphology and overjet in untreated Class II subjects. MATERIALS AND METHODS: The lateral cephalograms of 140 untreated Class II subjects (68 males and 72 females) between 8 and 11 years of age were divided into three groups based on their overjet value as measured on study casts: Group I normal overjet (less than 3 mm), Group II increased overjet (more than 3 mm but less than or equal to 6 mm), and Group III extreme overjet (more than 6 mm). Mean values and standard deviations of 28 variables measured on lateral cephalograms were calculated. Differences between the three groups were tested using one-way analysis of variance (ANOVA), followed by Bonferroni tests. Additionally, cephalometric differences between groups and available normal values for the Syrian population were evaluated using an independent t-test. RESULTS: Subjects with normal overjet showed a horizontal facial pattern and posterior inclination of the maxilla, whereas increased overjet subjects exhibited a neutral facial pattern. In contrast, subjects with extreme overjet had a vertical facial pattern and anterior inclination of the maxilla. The mandible was retrognathic and the maxilla was normally positioned in the three groups. CONCLUSIONS: A positive association was found between the overjet and the tendency toward a hyperdivergent pattern.
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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) | 0.000 | 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) | 0.003 | 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".