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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".