An Innovative Miniscrew-Based Jig for Unilateral Total Arch Mesialization
Bibliographic record
Abstract
To describe the management of a case with skewed dental arches and midlines deviation using a novel approach based on temporary anchorage devices (TADs) to derogate the deviated arches by unilateral total arch mesialization. This study presents the treatment course of a 25-year-old man undergone a previous improper orthodontic treatment with unnecessary extraction of the upper and lower right first premolar teeth leading to asymmetric dental arches. The patient complained of dental crowding, an unaesthetic smile arch, and the maxillary and mandibular dental midline deviation. The proposed treatment plan goals were decrowding and correction of both arches asymmetry and achievement of coincident upper and lower dental midlines. The corrective treatment plan consisted of total arch mesialzation in both dental arches using an innovative TAD assisted jig. The overall treatment was accomplished in 28 months with significant improvement in facial aesthetic and reasonable periodontal status. This innovative clinical biomechanical setup of miniscrew-anchored sliding jig helped us achieve all the treatment goals (total dental arches mesialization, dental midline deviation correction, and ideal final aesthetic and occlusion) in a reasonable period of time. With proper planning, innovative designs based on TADs are effective alternatives in challenging cases such as uni or bilateral dental arches mesialization, and dental asymmetry correction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".