Effectiveness of clear aligner therapy for orthodontic treatment: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To analyse through a systematic review the effectiveness of clear aligners by assessing: (a) predictability of clear aligners and (b) treatment outcome comparison of clear aligner therapy with fixed appliance therapy. METHODS: An electronic search was made from January 2014 to April 2019 using MEDLINE, Embase, Web of Science and LILACS databases without any limitations on language. Three reviewers independently assessed the articles. Quality assessment of observational studies and randomized control trial was done by using the ROBINS tool and Cochrane risk of bias tool, respectively. GRADE instrument was used to assess certainty level for each identified outcome. RESULTS: Seven eligible articles (one randomized controlled trial and six retrospective cohort) were included in our systematic review. Most of the studies (six out of seven) had a moderate risk of bias and one had a high risk of bias. CONCLUSIONS: 'Low to moderate level' of certainty in regard to specific clear aligner therapy tooth movements' efficiency was identified. Clear aligners may produce clinically acceptable outcomes that could be comparable to fixed appliance therapy for buccolingual inclination of upper and lower incisors in mild to moderate malocclusions. However, not all potential clinical scenarios have been assessed in the included studies. Most of the tooth movements may not be predictable enough to be accomplished with only one set of trays with clear aligners despite the recent advances in technology.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".