Comparison of the Effects of Photobiomodulation with Different Lasers on Orthodontic Movement and Reduction of the Treatment Time with Fixed Appliances in Novel Scientific Reports: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
Background: The duration of orthodontic treatment is one of the most important aspects considered by patients. Photobiomodulation (PBM) depends upon the exposure of the tissue to particular, therapeutic wavelengths of light in the “therapeutic window” (from 600 to 1200 nm). PBM increases cell metabolism, which leads to higher ATP production. Increasing the amount of ATP in well-vascularized bone cells promotes cell proliferation and differentiation, creating a favorable environment for tooth movement. Objective: The aim of the study is to discuss and compare the use of PBM in accelerating the orthodontic movement and reducing the time of treatment. Materials and methods: A systematic review was conducted. Literature searches were performed using Medline (PubMed), Web of Science, and Scopus (from September 13 to September 20, 2019). The quality assessment was performed using the Jadad scale for reporting randomized controlled trials for randomized clinical trial and randomized control clinical trial studies, and the Newcastle/Ottawa Quality Assessment Form for case/control studies. Results: Thirty-three articles from PubMed, 46 from Scopus, 5 from Web of Science were selected. After removal of duplicates, 82 articles were analyzed. Subsequently, 74 articles were excluded because they did not meet the inclusion criteria. The remaining eight articles were included in the qualitative synthesis. Conclusions and summary: PBM is an efficient, effective, and noninvasive method to accelerate orthodontic tooth movement. PBM should be introduced into the daily practice of treating various malocclusions as an additional procedure. Intraoral application gives better results and its introduction to treatment seems more reasonable.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".