Adjunctive Vibration For Orthodontic Pain Reduction: A Meta-Analysis And Systematic Review
Bibliographic record
Abstract
Objective: To evaluate the effects of adjunctive vibration on pain reduction in orthodontic treatment with fixed appliances or clear aligners, based on 10 clinical studies (N=512; 6 RCTs, 4 non-randomized). Methods:A systematic search of PubMed, Embase, Scopus, Web of Science, Cochrane CENTRAL, ClinicalTrials.gov,and WHO ICTRP (inception to 24 September 2025) identified 1150 records.After deduplication, 780 were screened, 85 assessed in full text, and 10 studies included.Studies were analyzed for vibration parameters, pain outcomes (VAS at 24/48/72 h, analgesic use), and risk of bias (RoB 2, ROBINS-I).Meta-analysis was planned but not feasible due to heterogeneity (I²>75%).Results: Meta-analysis infeasible due to high heterogeneity (I² > 75%).Four RCTs (N=232) found no pain reduction with low-frequency vibration (LFV, ~30 Hz; VAS differences -0.3 to +0.1, p>0.05).Two RCTs and three non-randomized studies (N=280) reported reduced pain with high-frequency vibration (HFV, ~100-133 Hz; VAS 0.5-1.2lower, p<0.05) at 24-48 h; one included low-level laser therapy (LLLT).HFV demonstrated moderate heterogeneity (I²=82%).In aligner studies, HFV reduced peak pain by 15-25% and analgesic use by ~20%.No increased adverse events were reported.Conclusions: Adjunctive HFV (~100-133 Hz, 3-5 min/day) provides modest short-term pain relief (≈0.5-1.2VAS units at 24-48 h) and reduced analgesic use, particularly in aligners, while LFV (~30 Hz) is ineffective.Limitations include small sample sizes, high heterogeneity, and short follow-ups.Larger multicenter RCTs with standardized outcomes are needed to confirm efficacy and optimize dosing.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".