Adjunctive therapies in orthodontics: a scoping systematic review
Bibliographic record
Abstract
BACKGROUND: Orthodontic tooth movement (OTM) induces physiological and sometimes pathological inflammation in periodontal tissues. This review evaluates the effectiveness of low-level laser therapy (LLLT), vibrational therapy, and probiotics as adjunctive treatments for managing inflammation, pain, and the duration of OTM. METHODS: Medline via OVID, Cochrane, EMBASE, and Web of Science databases were utilized to identify randomized controlled trials (RCTs) published between January 1990 and November 2023. Studies were selected based on their evaluation of LLLT, vibrational therapy, and probiotics as adjuncts in fixed orthodontic treatment. RESULTS: LLLT shows promise in enhancing orthodontic tooth movement by accelerating tooth movement and potentially reducing pain. However, disparate study outcomes indicate a need for standardized application protocols. The efficacy of vibrational therapy as an adjunct in OTM remains inconclusive. Some studies in this regard indicate a significant acceleration in OTM but most did not. Probiotic therapy shows potential to improve oral microbiota balance and inflammation but requires more rigorous studies to determine its efficacy and optimal administration methods. CONCLUSION: Future research should focus on establishing standardized guidelines and protocols to achieve consistent and reliable outcomes across these adjunctive therapies.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".