Effect of Ligation Method on Maxillary Arch Force/Moment Systems for a Simulated Lingual Incisor Malalignment
Bibliographic record
Abstract
INTRODUCTION: The objectives of this study were to determine whether there is a difference in the magnitude of forces and moments produced by elastic ligation when compared to passive ligation, and whether these forces and moments propagate differently along the arch for the two ligation types. A lingual incisor malalignment was used in this study. METHODS: The Orthodontic Simulator (OSIM) was used to quantify the three-dimensional forces and moments applied on the teeth given a lingually displaced incisor. A repeated measures MANOVA was performed to statistically analyze the data. RESULTS: The interaction factor illustrated convincing evidence that there is a difference in maximum force and moment values for all outcome variables between ligation types considering all tooth positions along the arch. The mean differences for FX and FY between ligation types were found to be clinically significant, with values for elastic ligation consistently higher than passive ligation. CONCLUSION: It was found that the maximum forces and moments produced by elastic ligation are greater than those produced by passive ligation and that the magnitude of this difference for the mesiodistal and buccolingual forces is clinically relevant. Additionally, it was determined that elastic ligation causes forces and moments to propagate further along the arch than passive ligation for all outcome variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".