Effect of high-frequency vibration on orthodontic tooth movement and bone density
Bibliographic record
Abstract
OBJECTIVES: Previous reports have shown that high-frequency vibration can increase bone remodeling and accelerate tooth movement. The aim of this study was to evaluate the effects of high-frequency vibration on treatment phase tooth movement, and post-treatment bone density at initiation of retention, with cone-beam computed tomography (CBCT).MATERIALS AND METHODS: Thirty patients with initial Class I skeletal relationships, initial minimum-moderate crowding (3–5 mm), treated to completion with clear aligners and adjunctive high-frequency vibration, (HFV group) or no vibration, (Control group) were evaluated. The patients were instructed to change aligners as soon as they become loose. Changes in bone density associated with orthodontic treatment were evaluated using i-CAT cone-beam computed tomography (CBCT) and InVivo Anatomage® software to quantify density using Hounsfield units (HU) between treated teeth in 10 different regions. HU values were averaged and compared against baseline (T1) and between the groups at initiation of retention (T2).RESULTS: The average time for aligner change was 5.2 days in the HFV group, and 8.7 days in the control group (P = 0.0001). There was significant T1 to T2 increase of HU values in the upper arch (P = 0.0001) and the lower arch (P = 0.008) in the HFV group. There was no significant change in average HU values in the upper (P = 0.83) or lower arches (P = 0.33) in the control group. The intergroup comparison revealed a significant difference in the upper, (P = 0.0001) and lower arches (P = 0.007).CONCLUSION: High-frequency vibration adjunctive to clear aligners, allowed early aligner changes that led to shorter treatment time in minimum-moderate crowded cases. At initiation of retention, the HFV group demonstrated statistically significant increase as compared with pre-treatment bone density, whereas control subjects showed no significant change from pre-treatment bone density.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".