Effect of the application of high-frequency mechanical vibration on tooth length concurrent with orthodontic treatment using clear aligners: A retrospective study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the possible change in teeth lengths as an indicator of orthodontically induced tooth root resorption (OITRR) after high-frequency mechanical vibration (HFV) treatment concurrent with Invisalign Smart Track® aligners as evaluated by cone beam computed tomography (CBCT).MATERIALS AND METHODS: The sample, composed of 30 patients with an average age of 26 ± 11 years and Class I malocclusion with an initial anterior crowding ranging from 3 to 5 mm, was divided equally into two groups; Group I received adjunctive high-frequency mechanical vibration (HFV); Group II, the control, did not receive adjunctive mechanical treatment. The maxillary incisor's teeth lengths were measured using Mimics software before (T1) and after (T2) treatment. All data were analyzed using Student's t-test. Reliability testing was completed by randomly selecting 10 patients' CBCTs, and their teeth lengths were measured twice by the same investigator over a 15-day interval and compared confirming intra-operator accuracy.RESULTS: The control group showed a statistically significant decrease in tooth lengths compared to the HFV group which showed nonstatistically significant change of tooth lengths.CONCLUSION: Patients treated with HFV showed minimum tooth length changes after treatment, which may indicate that HFV can reduce OITRR with treatment using clear aligners.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".