The Impact of Therapeutic Ultrasound on Bone Radio Density Following Orthodontic Treatment with Clear Aligners: A Preliminary Study
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the impact of Low-Intensity Pulsed Ultrasound (LIPUS) on bone radio density in patients undergoing orthodontic treatment with clear aligners, aiming to enhance bone remodeling and improve treatment stability. METHODS: This retrospective study included 68 participants divided into two groups: 34 treated with LIPUS and 34 in a control group. Bone radio density was measured using Hounsfield units from CBCT scans before and after treatment. Statistical analyses included Mann-Whitney U tests and paired ttests. RESULTS: The average age was 29.85 ± 14.85 years in the control group and 36.29 ± 12.78 years in the LIPUS group. Bone radio density in the upper arch of the LIPUS group significantly increased from 444.6 HU to 751.3 HU (p < 0.001), while the control group showed a slight decrease in the upper arch (657.4 HU to 650.5 HU, p = 0.86). In the lower arch, a similar trend was observed in the LIPUS group, with an increase from 767.7 HU to 823.4 HU (p = 0.17), though not statistically significant. There were no significant differences in post-treatment ABO DI scores between groups, suggesting equivalent effectiveness in achieving orthodontic outcomes. CONCLUSION: LIPUS with clear aligners seems promising in enhancing bone radio density, indicating an improved bone remodeling effect. This highlights LIPUS's potential as a beneficial adjunct in orthodontic treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".