Efficacy and Predictability of Maxillary and Mandibular Dental Arch Expansion with Clear Aligners in Prepuberal Subjects: A Digital Retrospective Analysis
Bibliographic record
Abstract
Background/Objectives: Previous studies on clear aligner therapy (CAT) in mixed dentition primarily focused on the predictability of maxillary arch expansion. However, limited evidence is available regarding mandibular arch changes, particularly in relation to inter-arch coordination. This study aims to evaluate the effectiveness and predictability of dental expansion in both the upper and lower arches using Invisalign First® aligners. Methods: A retrospective analysis was conducted with 15 participants. Dental expansions were assessed before and after treatment using iTero intraoral scans processed with 3D analysis software. Measurements were compared to the predicted movements planned in ClinCheck®. Data normality was verified (Shapiro–Wilk test), descriptive statistics were calculated, and paired t-tests were performed to compare clinical and predicted expansions, with significance set at 0.05. Results: Clear aligners achieved effective dento-alveolar expansion in both arches. Predictability was higher at the cusp level than at the gingival level, indicating a tendency toward tipping movements rather than bodily expansion. The study also highlighted mandibular expansion outcomes and gingival-level discrepancies, providing new insights compared to the previous literature. Minor differences between predicted and achieved movements were observed, partly attributable to natural growth and deciduous tooth exfoliation. Conclusions: Clear aligners are effective in achieving maxillary and mandibular arch expansion in mixed dentition, with good predictability at the coronal level. Overengineering buccal root torque may help promote bodily expansion and reduce cuspal–gingival discrepancies. Further studies with larger sample sizes are needed to optimize treatment planning and predictability.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".