The influence of facial pattern on skeletal class I subjects- a cephalometric analysis
Bibliographic record
Abstract
Objective: The purpose of this study was to assess the correlations between the Wits appraisal (using maxillomandibular bisector as the occlusal plane), ANB analysis and facial pattern in skeletal Class I subjects Materials and methods: A retrospective chart review was completed on 100 Class I subjects according to the ANB angle. The maxillomandibular bisector (MMB) was used as the occlusal plane to determine the sagittal maxillomandibular relationship according to the Wits appraisal. Four additional measurements (mandibular plane angle, Y-axis, lower facial height and facial axis) associated with facial pattern were measured to determine whether the Wits or ANB analysis is correlated in classifying skeletal and facial patterns Results: A weak correlation was found between ANB and Wits (r=0.38) that was statistically significant (p<0.05). Correlations between ANB and all facial pattern measurements were also weak, but they were not statistically significant (p>0.05). Moreover, associations were found between Wits and facial pattern measurements ranging from low to high (-0.05 to 0.57) and were all statistically significant (p<0.05). The strongest correlations were between facial axis (r=0.57), MPA (r=-0.46) and Wits. A moderate correlation was found between lower facial height and Wits (r=-0.331). There were no substantive differences between males and females. Conclusions: The Wits appraisal using the maxillomandibular bisector occlusal plane is a valid indicator of the anteroposterior discrepancy and facial pattern. Wits may be a more accurate predictor of facial pattern vs. ANB. However, caution must be exercised in trying to relate Wits appraisal to the gold standard of the ANB angle.
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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 | 0.001 |
| 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) | 0.003 | 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".