Impact of post-orthodontic dental occlusion on masticatory performance and chewing efficiency
Bibliographic record
Abstract
BACKGROUND: Whether precise orthodontic detailing of occlusion impacts masticatory function is unknown. In this study, we aimed to assess the impact of post-orthodontic dental occlusion on masticatory performance and chewing efficiency. MATERIALS AND METHODS: Fifty-four adults who completed orthodontic treatment were categorized into two groups using the American Board of Orthodontics (ABO) model grading system: one meeting ABO standards (ABO, N = 29), the other failing to meet them (non-ABO, N = 25). The electromyographic (EMG) signals of the anterior temporalis (AT) and superficial masseter muscles were recorded bilaterally during static (clenching) and dynamic (gum chewing) tests. Chewing efficiency was measured by calculating the median particle size (MPS) and broadness of particle distribution (BPD) after five chewing trials of experimental silicone food at a standardized chewing rate. RESULTS: Participants of the ABO group had a slightly more symmetric activation of the AT muscles during clenching (P = 0.016) and chewed a gum at a slower rate (P = 0.030). During the standardized chewing test with silicone food, ABO subjects had slightly greater EMG potentials at all muscle locations than non-ABO individuals (all P < 0.05). MPS and BDP did not differ significantly between groups (all P > 0.05). LIMITATIONS: The severity of the initial malocclusion of the study participants was not in the statistical model as a potential confounder on the outcome measures. CONCLUSIONS: Meeting ABO standards contributes to a slightly more balanced activation of the temporalis muscles during clenching and more efficient muscle recruitment during chewing but does not improve chewing efficiency.
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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 | 0.000 | 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) | 0.003 | 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".