A New Anchorage Device for Orthodontic Applications
Bibliographic record
Abstract
In orthodontic treatment, anchorage is the most important element that affects the treatment’s success. To improve the load bearing capacity of the anchorage there are several devices developed in recent decades such as midpalatal implants and onplants but they also have limitation on directions of applied load and their support position adjustability. The purpose of this study was to investigate the efficiency of a new anchorage device by analyzing the load-bearing and stress distribution among the cortical and cancellous bones of the mandible as well as the anchorage system components using nonlinear 3D Finite Element (FE) method. The new device is composed of an adjustable stainless steel plate equipped with bracket and mounted with two titanium mini-screws into the mandible. The response of this new system was compared to an isolated mini-screw system under different loading scenarios. A maximum of 500gr force was applied in different directions on the bracket and the isolated mini-screw head to simulate the orthodontic loading. Using the new anchorage device reduced von-Mises stress in the whole structure approximately by 50% comparing to the isolated mini-screw. In the cortical bone and depending on the direction of the applied force, von-Mises stress decreased from 6 to 3MPa under vertical shear force and from 6 to 1.5MPa under horizontal and inclined shear forces. In the cancellous bone the stress decreased similarly as in the cortical bone from 0.6 to ≈0.3MPa under horizontal and inclined shear. Under vertical shear force the decrease was less significant from 0.57MPa to 0.5MPa. This new device while offering wide fields of orthodontic forces applications thanks to its bracket provides the same resistive force (500gr) as the isolated mini-screw with much lower stresses in the bone and anchorage implant as well. The next step is to investigate the efficiency of this new device in the teeth movement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".