A comparison of torque expression between stainless steel, titanium molybdenum alloy, and copper nickel titanium wires in metallic self-ligating brackets
Bibliographic record
Abstract
OBJECTIVE: The force moment providing rotation of the tooth around the x-axis (buccal-lingual) is referred to as torque expression in orthodontic literature. Many factors affect torque expression, including the wire material characteristics. This investigation aims to provide an experimental study into and comparison of the torque expression between wire types. MATERIALS AND METHODS: With a worm-gear-driven torquing apparatus, wire was torqued while a bracket mounted on a six-axis load cell was engaged. Three 0.019 x 0.0195 inch wire (stainless steel, titanium molybdenum alloy [TMA], copper nickel titanium [CuNiTi]), and three 0.022 inch slot bracket combinations (Damon 3MX, In-Ovation-R, SPEED) were compared. RESULTS: At low twist angles (<12 degrees), the differences in torque expression between wires were not statistically significant. At twist angles over 24 degrees, stainless steel wire yielded 1.5 to 2 times the torque expression of TMA and 2.5 to 3 times that of nickel titanium (NiTi). At high angles of torsion (over 40 degrees) with a stiff wire material, loss of linear torque expression sometimes occurred. CONCLUSIONS: Stainless steel has the largest torque expression, followed by TMA and then NiTi.
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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 | 0.001 | 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".