Influence of maxillary posterior dentoalveolar discrepancy on angulation of maxillary molars in individuals with skeletal open bite
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine the effect of the maxillary posterior dentoalveolar discrepancy (MPDD) on the angulation of maxillary molars in open bite subjects. METHODS: Pre-treatment lateral cephalograms of 90 young adults with skeletal open bite were examined. The sample initially included six groups categorized according to MPDD condition (present or absent) and sagittal skeletal facial growth patterns (classes I, II, or III). Then, the sample was separated into two groups according to MPDD (present = 50, absent = 40). When the eruption of the maxillary third molar was apparently blocked by the presence of an erupted second molar, a MPDD was considered. Maxillary molar angulation was measured. Independent T test was performed to determine differences between the groups considering MPDD condition. Principal component analysis (PCA) and multivariate analysis (MANCOVA) test were also developed. RESULTS: A decreased molar angulation was found in all groups with MPDD (overall p < 0.001, class I-p < 0.001, class II-p < 0.001, and class III-p < 0.05). The maxillary first and second molars angulations were lower between approximately 7° and 14° in cases with posterior discrepancy. The PCA was used to reduce the number of initial cephalometric variables; thereafter, a MANCOVA test was applied. Significance was only found for MPDD (p < 0.001), APDI (p = 0.001), and ratio (A'6'/A'P') (p = 0.026) for maxillary first molar angulation and APDI (p = 0.011) and MPDD (p < 0.001) for maxillary second molar angulation. CONCLUSIONS: The MPDD generates a major mesial displacement of the second and first molar roots with a concurrent simultaneous distal angulation of the associated crowns in individuals with skeletal open bite.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".