The effect of buccal–lingual slot dimension size on third-order torque response
Bibliographic record
Abstract
Introduction: The focus of the presented study was to investigate the effect of buccal-lingual (B-L) orthodontic bracket slot dimension on third-order torque mechanics. Materials and methods: Three types of orthodontic brackets and two archwire sizes were considered. Ortho Classic H4 (0.026″ B-L slot, passive), Ormco Damon Q (0.028″ B-L slot, passive), and In-Ovation R (0.028″ slot, active) brackets were tested using 0.017″ × 0.025″ and 0.019″ × 0.025″ beta-titanium archwires. An in vitro orthodontic torque simulator (OTS) was used to rotate archwires relative to a single bracket while recording forces and moments in three directions. For each bracket-archwire combination, a total of n = 47 samples were tested. Repeated measures analysis of variance between brackets was conducted for third-order torque values at 3° increments between 9° and 30° during loading and unloading for each archwire size. Results: Statistically significant differences between H4 and Q brackets were only found for 0.017″ × 0.025″ archwires during loading, and 0.019″ × 0.025″ archwires during unloading. Conversely, differences between H4 and R brackets were found for both archwires during loading and unloading phases. Finally, when using a 0.017″ × 0.025″ archwire the H4 brackets reached the 5 Nmm threshold before R and Q brackets; however, there was little difference found when using a 0.019″ × 0.025″ archwire. Conclusions: The concept of using a smaller B-L bracket slot dimension in orthodontic treatment showed it may theoretically allow for more options, primarily using smaller archwires to correct third-order rotational misalignments. However, it is suspected that bracket material limitations and added loading on the door currently prevent this from being clinically applicable.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".