Effect of low-intensity pulsed ultrasound on orthodontically induced root resorption caused by torque: A prospective, double-blind, controlled clinical trial
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effects of low-intensity pulsed ultrasound (LIPUS) on orthodontically induced tooth root resorption caused by torque in human subjects. MATERIALS AND METHODS: Ten healthy patients (12-35 years of age) who required extraction of all first premolars as a part of their routine orthodontic treatment were recruited. A 15° twist was applied in the arch wire using 0.019 × 0.025-inch TMA in a 0.022-inch bracket system (Synergy R) that produced a buccal root torque of approximately 5 N/mm at the bracket level. Using a split mouth design, randomization, and blinding, one side of the arch received LIPUS for 20 minutes per day for 4 weeks at an incident intensity of 30 mW/cm(2) of the transducers' surface area. The other side served as a self-control, which received a sham transducer. After 4 weeks, all first premolars were extracted and micro-computed tomographic analysis was performed on these extracted teeth. A linear mixed-model statistical analysis was used. RESULTS: LIPUS-treated teeth showed significantly less total volume of resorption lacunae compared to control teeth by a mean difference of (0.54 ± 0.09 mm(3)) (P < .001) and percentage of root resorption by a mean difference of (0.33 ± 0.05 mm(3)) (P < .001). In addition, significantly fewer resorption lacunae were found on all root surfaces in the LIPUS group compared to the control except in the instance of the distal surface. LIMITATIONS: This study was performed on limited number of cases during a 4-week period. CONCLUSIONS: LIPUS minimizes root resorption when applied during torque tooth movement over a 4-week period.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".