In Vitro Biomechanics of Attachment Use and Their Placement for Extrusive Tooth Movement by Aligner Mechanotherapy
Bibliographic record
Abstract
The objective of the presented study was to evaluate the biomechanics of aligners utilising attachments and the effect of their placement for extrusion. An in vitro Orthodontic SIMulator (OSIM) was used to measure forces and moments produced through aligners incorporating attachments. The maxillary teeth on OSIM were digitally scanned to generate an STL model that was modified by placing a rectangular shaped attachment. Four models were designed with variation in attachment placement: Model NA-no attachment; model BA-buccal attachment; model LA-lingual attachment; model BL-both buccal and lingual attachments. Aligners (n = 30/model) were fabricated using a 0.75 mm thick polyethylene terephthalate material using a Biostar machine following the manufacturer's recommendations. A one-way MANOVA, followed by one-way ANOVA was used to test the effect of models on occlusogingival force (Fz) and mesiodistal moment (Mx) at 0.20 mm of gingival displacement of the left lateral incisor. The BL model exerted maximum Fz (1.22 ± 0.20 N), followed by models BA (1.18 ± 0.25 N) and LA (1.07 ± 0.19 N). The model NA exerted a negligible Fz (0.14 ± 0.08 N) that was statistically different from all other models. The models BA and LA generated Mx that has the tendency to tip the crown of tooth lingually (10.00 ± 3.12 Nmm) and buccally (-1.29 ± 2.26 Nmm), respectively. The models show statistically significant differences between models BL, BA and LA. There was no statistically significant difference between models BL and NA. The experimental evidence suggest that attachments on the lateral incisor could improve the predictability of extrusion forces applied with aligners. The models BL and LA provided favourable biomechanics by generating clinically significant extrusion forces without significant tipping of the lateral incisor tooth.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".