Distal Upper Molar Force Distribution With Clear Aligners Using Different Anterior Teeth Anchorage Setups: A Finite Element Study
Bibliographic record
Abstract
OBJECTIVE: The notion that clear aligners alone can distalize upper molars without affecting anterior teeth is inaccurate. Although strategies such as Class II elastics, tooth-movement sequencing, and attachment variations have been investigated to mitigate unwanted side effects, temporary anchorage devices have demonstrated potential for maintaining anchorage during molar distalization. This study used Finite Element Analysis to evaluate different setups for distalizing one or both upper molars, comparing passive anchorage (ligature tie) and active anchorage (1.66 N), and assessing the presence of vertical attachments. MATERIALS AND METHODS: Six models were generated with 0.2 mm distal activation for molar distalization. These models varied by premolar/M attachments and anchorage type-active (1.66 N) or passive-applied from extra-alveolar screws to canine buttons. RESULTS: All setups distalized the second molars, but passive anchorage demonstrated greater efficiency and fewer side effects. Passive systems achieved over 90% distalization-to-anchorage loss ratios, compared with 65% with active forces. Passive setups also minimised unintended anterior movement and enabled distal canine movement following molars-an advantageous outcome. Vertical attachments had minimal impact. X-axis (midline) movement predominantly affected canines, particularly with active anchorage. Anterior intrusion on the Z-axis was reduced with passive systems. CONCLUSION: Active anchorage forces may deform aligners and compromise control, whereas passive anchorage-similar to a ligature wire applied to anterior teeth-supports planned movement without disrupting biomechanics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".