Shear Bond Strength of different accessories used to traction impacted teeth
Bibliographic record
Abstract
Objectives: To evaluate the shear bond strength and the adhesive remnant index (ARI) of different orthodontic accessories used for applying traction on impacted teeth.Methods: 120 bovine incisors were used.Initially, all teeth were submitted to prophylaxis, subsequent etching with 37% phosphoric acid, application of adhesive and light polymerization.Afterwards, these teeth were randomly divided into eight groups: (1) composite lingual button; (2) hook for application of traction on impacted teeth; (3) hook with chain; (4) cleat; (5) brackets; (6) convex lingual button; (7) concave lingual button; and (8) orthodontic mesh.The groups were submitted to shear tests in a universal test machine, and ARI evaluation. Results:The group of orthodontic mesh (8) presented the best shear bond strength results with statistically significant differences comparing with the composite lingual button (p<0.001),hooks for application of traction on impacted teeth (p=0.002),hooks with chain (p=0.001),cleat (p=0.011),brackets (p< 0.001), convex lingual button (p=0.003) and convex lingual button (p<0.001).The highest mean ARI values were also obtained for the mesh group, with statistically significant differences comparing with the composite lingual button (p=0.008),cleat (p=0.004),brackets (p=0.001),convex lingual button (p=0.017) and concave lingual button (p=0.005). Conclusion:The greatest adhesion forces were obtained with the orthodontic mesh, which was statistically different from all other groups, and the lowest adhesion forces with the composite lingual button.(Rev Port Estomatol Med Dent Cir Maxilofac.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".