Archwire depth is a significant parameter in the frictional resistance of active and interactive, but not passive, self-ligating brackets
Bibliographic record
Abstract
OBJECTIVE: To elucidate some of the parameters determining the frictional behavior of passive, active, and interactive self-ligating brackets during sliding mechanics. MATERIALS AND METHODS: A custom jig mimicking the three-dimensional tooth movements that occur during sliding mechanics and an Instron testing machine were used to determine the resistance to sliding of six different self-ligating brackets (SPEED, In-Ovation C, CarrièreSLB, ClaritySL, SmartClip, VisionLP). Each bracket was tested on three rectangular stainless steel wires: 0.017 × 0.022 inch, 0.017 × 0.025 inch, and 0.019 × 0.025 inch. A two-way balanced analysis of variance was used for statistical analysis. RESULTS: The four passive self-ligating brackets (CarrièreSLB, ClaritySL, SmartClip, VisionLP) displayed significantly lower frictional resistance (P < .001) than the remaining brackets, which featured an active (SPEED) or interactive (In-Ovation C) clip. The SPEED bracket consistently demonstrated the highest resistance to sliding. CONCLUSIONS: The mode of self-ligation appears to be the primary variable in determining the frictional behavior of orthodontic brackets undergoing sliding mechanics. Passive self-ligating brackets demonstrated significantly lower frictional resistance to sliding. With regard to the resistance of active self-ligating brackets, the depth (buccolingual thickness) of the wire had a more significant influence than its height.
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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) | 0.000 | 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) | 0.002 | 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".