Efficacy and safety of different interventions to accelerate maxillary canine retraction following premolar extraction: A systematic review and network meta‐analysis
Bibliographic record
Abstract
Decreasing orthodontic treatment duration is at the forefront of innovation for clinical orthodontics. This network meta-analysis aimed to determine the relative efficacy and safety of treatments for accelerated orthodontic tooth movement (OTM) in patients undergoing extraction of maxillary first premolars followed by canine retraction in any orthodontic setting. MEDLINE, EMBASE, Cochrane CENTRAL, CINAHL and SCOPUS were searched (from inception to 20 April 2020). Study selection and data extraction were performed in duplicate. Eligible randomized controlled trials (RCTs) were meta-analysed to estimate the rate of tooth movement, 95% credible interval and surface under the cumulative ranking curve (SUCRA) in the first 3 months following the application of the adjunctive accelerative method. Eligible RCTs were assessed by Cochrane risk of bias tool, and quality of evidence was assessed by GRADE approach, obtained from CINeMA web application. Interventions were ranked for efficacy and reviewed for safety. Nineteen studies pertaining to eight interventions, with data from 415 patients were included. Quality of evidence was very low to moderate. Very low-to low-quality evidence suggests that corticotomy is an efficacious and safe adjunctive treatment to accelerate OTM in comparison with conventional treatment in the first 2 months of treatment. Low-quality evidence suggests that piezocision and micro-osteoperforations (MOP) are efficacious and safe adjunctive treatments only in the first month of treatment. Frequent MOP in conjunction with low-level laser therapy appeared to be an efficacious and safe adjunctive treatment only in the first month following its initial application but not thereafter.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".