Effectiveness of en masse versus two-step retraction: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background This review aims to compare the effectiveness of en masse and two-step retraction methods during orthodontic space closure regarding anchorage preservation and anterior segment retraction and to assess their effect on the duration of treatment and root resorption. Methods An electronic search for potentially eligible randomized controlled trials and prospective controlled trials was performed in five electronic databases up to July 2017. The process of study selection, data extraction, and quality assessment was performed by two reviewers independently. A narrative review is presented in addition to a quantitative synthesis of the pooled results where possible. The Cochrane risk of bias tool and the Newcastle-Ottawa Scale were used for the methodological quality assessment of the included studies. Results Eight studies were included in the qualitative synthesis in this review. Four studies were included in the quantitative synthesis. En masse/miniscrew combination showed a statistically significant standard mean difference regarding anchorage preservation − 2.55 mm (95% CI − 2.99 to − 2.11) and the amount of upper incisor retraction − 0.38 mm (95% CI − 0.70 to − 0.06) when compared to a two-step/conventional anchorage combination. Qualitative synthesis suggested that en masse retraction requires less time than two-step retraction with no difference in the amount of root resorption. Conclusions Both en masse and two-step retraction methods are effective during the space closure phase. The en masse/miniscrew combination is superior to the two-step/conventional anchorage combination with regard to anchorage preservation and amount of retraction. Limited evidence suggests that anchorage reinforcement with a headgear produces similar results with both retraction methods. Limited evidence also suggests that en masse retraction may require less time and that no significant differences exist in the amount of root resorption between the two methods.
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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 | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchResearch integrity Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".