Effects of piezocision in orthodontic tooth movement: A systematic review of comparative studies
Bibliographic record
Abstract
BACKGROUND: The aim of this systematic review was to evaluate the effects of piezocision in accelerating orthodontic tooth movement (OTM) and its possible adverse effects. MATERIAL AND METHODS: The Databases Medline, Embase, CENTRAL and LILACS were searched until March 2019, for randomized controlled trials (RCTs) and controlled clinical trials (CCTs) that used piezocision associated with orthodontic treatment. A manual search was also performed. The search, studies selection, assessment of risk of bias and data collection were carried out by two independent reviewers. RESULTS: Eleven publications were included in this review (4 CCTs and 7 RCTs). No study presented low risk of bias. Different types of tooth movement were evaluated: lower anterior alignment, en-masse retraction, overall orthodontic treatment and canine distalization. A total of 240 participants were analyzed in the included studies. Seven studies found significant acceleration in the piezocision group, while two studies found no differences. Adverse effects regarding patient's satisfaction, pain perception, or worsening of periodontal parameters were not observed. There was no consensus concerning anchorage loss and root resorption. CONCLUSIONS: Piezosurgery, tooth movement techniques, orthodontics.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".