Overview of systematic reviews and meta-analyses assessing the predictability and clinical effectiveness of clear aligner therapy
Bibliographic record
Abstract
This study conducted an overview of systematic reviews (SRs) and included randomized controlled trials (RCTs) to evaluate the predictability of tooth movements and clinical effectiveness of clear aligner therapy (CAT) compared to fixed appliances (FAs). The PRISMA guidelines were followed, and seven electronic databases were systematically searched for publications up to March 15, 2022. The quality of the included SRs and RCTs was assessed using the AMSTAR-2 and RoB-2 tools, respectively. Initially, 18 SRs and 2 RCTs were identified, and after quality assessments, 11 SRs and 1 RCT were retained for data synthesis. The comparison between software-predicted and actual tooth movements indicated that CAT's accuracy in predicting rotational movements, especially for canines, was not reliable. Horizontal movements, particularly in the upper incisors, were more predictable, while vertical movements were less predictable. The overall American Board of Orthodontics (ABO) objective grading system (OGS) scores did not show a significant difference between the CAT and FAs groups, with a high heterogeneity of 90% (P<0.0001) and a confidence interval of -2.32 to 18.4. The current evidence level regarding the predictability of tooth movements and clinical effectiveness of CAT compared to conventional FAs is considered to be low to moderate. While CAT can be used for treating complex malocclusions, it tends to yield less accurate results than FAs.
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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 | 0.034 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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, 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".