Effect of Low‐Intensity Pulsed Ultrasound (LIPUS) on Alveolar Bone during Maxillary Expansion Using Clear Aligners
Bibliographic record
Abstract
The present study evaluated the possible effect of low‐intensity pulsed ultrasound (LIPUS) on buccal bone plate thickness and height after maxillary arch expansion using clear aligners. The cone beam computed tomography (CBCT) of before and immediately after maxillary arch expansion (3 mm per side) of 28 adult patients (18 in LIPUS group and 10 in control) (average age 36.2 ± 13.2 years old) was analyzed. The wearing protocol of clear aligners in the LIPUS group was to change the aligners every 4 to 5 days, while the wearing protocol in the control group (without LIPUS) was to change the aligners every 7 to 10 days. Bone thickness at 3 mm and 6 mm from the buccal alveolar bone crests, along with the measurements of buccal alveolar bone heights, was measured in standardized sagittal sections. Data were analyzed through paired sample t‐test and the Wilcoxon test. The results were given as mean ± standard deviation and 95% confidence intervals. p value < 0.05 was considered statistically significant. The results showed significant increase in bone height in both groups. However, comparison of both LIPUS and control groups showed no statistically significant difference in bone thickness or bone height. The results of this study showed that the use of LIPUS together with accelerated aligner tray change protocol did not affect alveolar bone integrity when compared to the control group.
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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) | 0.001 | 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".