Force loss in archwire-guided tooth movement of conventional and self-ligating brackets
Bibliographic record
Abstract
This study aimed to investigate the differences in the force loss during simulated archwire-guided canine retraction between various conventional and self-ligating brackets. Three types of orthodontic brackets have been investigated experimentally using a biomechanical set-up: 1. conventional ligating brackets (Victory Series and Mini-Taurus), 2. self-ligating brackets (SmartClip: passive self-ligating bracket, and Time3 and SPEED: active self-ligating brackets), and 3. a conventional low-friction bracket (Synergy). All brackets had a nominal 0.022″ slot size. The brackets were combined with three rectangular 0.019×0.025″ archwires: 1. Remanium (stainless steel), 2. Nitinol SE (nickel-titanium alloy, NiTi), and 3. Beta III Titanium (titanium-molybdenum alloy). Stainless steel ligatures were used with the conventional brackets. Archwire-guided tooth movement was simulated over a retraction path of up to 4mm using a superelastic NiTi coil spring (force: 1 N). Force loss was lowest for the Victory Series and SmartClip brackets in combination with the steel guiding archwire (35 and 37.6 per cent, respectively) and highest for the SPEED and Mini-Taurus brackets in combination with the titanium wire (73.7 and 64.4 per cent, respectively). Force loss gradually increased by 10 per cent for each bracket type in combination with the different wires in the following sequence: stainless steel, Nitinol, and beta-titanium. Self-ligating brackets did not show improved performance compared with conventional brackets. There was no consistent pattern of force loss when comparing conventional and self-ligating brackets or passive and active self-ligating brackets.
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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 | 0.001 |
| 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) | 0.001 | 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".