In vitro biomechanics of divot use, and their placement, in orthodontic aligner therapy
Bibliographic record
Abstract
INTRODUCTION: To evaluate biomechanics of an aligner utilizing divots and the effect of their vertical placement on the right maxillary central incisor. METHODS: An in vitro Orthodontic SIMulator (OSIM) was used to test forces and moments generated by aligners incorporating divots. The OSIM arch was scanned to generate a. STL version that was modified to create four models by placing divots on different positions of the right central maxillary incisor: GI - divots on gingival-third of lingual surface and incisal-third of labial surface; GM - divots on gingival-third of lingual surface and middle-third of labial surface; MI - divots on middle-third of lingual surface and incisal-third of labial surface; MM - divots on middle-third of lingual surface and middle-third of labial surface. Aligners (n = 30/model) were fabricated using a 0.75 mm thick polyethylene terephthalate material and Biostar® machine following the manufacturer's recommendations. A one-way MANOVA followed by one-way ANOVA (α = 0.05) was utilized to test effect of models on buccolingual force (Fy) and mesiodistal moment (Mx) at 0.20 mm of lingual displacement of the right maxillary central incisor. RESULTS: Mean Mx for GI (-5.68 ± 7.38 Nmm), GM (3.75 ± 5.54 Nmm), MI (-4.27 ± 1.48 Nmm) and MM (1.96 ± 0.99 Nmm) models showed statistical differences between GI and GM, GI and MM, GM and MI and MI and MM. GI exerted the largest Fy (1.87 ± 0.75 N) followed by GM (1.10 ± 0.47 N), MI (0.70 ± 0.23 N) and MM (0.28 ± 0.08 N) with significant differences between GI and GM, GI and MI, GI and MM and GM and MM models. CONCLUSIONS: Vertical divot placement on a right central incisor had a significant effect on aligner biomechanics. Buccolingual forces exerted by models GI, GM and MI were within the range suggested by literature for bodily tooth movement without major root tipping for GM and MI models.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".