Effect of varying anchorage force intensity on upper molar distalization using clear aligners: a finite element study
Bibliographic record
Abstract
INTRODUCTION: This study investigated the effects of maxillary molar distalization using clear aligners and skeletal anchorage, specifically examining the influence of varying anchorage force levels through 3D finite element modeling. METHOD: Eight models were developed, systematically varying anchorage force (1.66 N and 3.34 N) from infrazygomatic crest (IZC) screws, force application site (precision cuts or buttons), and the presence or absence of vertical rectangular attachments. A 0.2 mm activation was applied between the first and second molars. RESULTS: Results indicated that variations in anchorage force did not significantly alter the displacement of the second molars or anterior teeth (canines and central incisors) across the X, Y, and Z axes, provided the force application site and attachments remained constant. However, changing these variables led to observable differences in displacement. Notably, models employing precision cuts and vertical attachments showed second molar distalization, expansion, and extrusion. Additionally, canines displayed reduced mesial crown displacement and intrusion, while central incisors moved labially with less intrusion. CONCLUSIONS: Overall, none of the combinations tested were sufficient to prevent some anchorage loss or unwanted tooth movement. Variations in anchorage force did not significantly affect the extent of second molar distalization or anterior anchorage loss. However, the precision cuts and vertical attachments on molars and premolars resulted in different and more pronounced unwanted displacements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".