In vitro measurement of the initial forces and moments generated for a curve of Spee malocclusion with labial and lingual archwire forms
Bibliographic record
Abstract
OBJECTIVES: To compare the biomechanics of labial and lingual fixed orthodontic treatment options for a simulated curve of Spee malocclusion. MATERIALS AND METHODS: An in vitro electromechanical orthodontic simulator was used to measure the three-dimensional forces and moments on each tooth of a mandibular arch. Labial and lingual brackets, both with 0.018-inch slot sizes, were bonded to mechanical teeth. Three archwire forms were considered: labial straight, lingual straight, and lingual mushroom. The simulator was set in a passive levelled position with 0.016-inch × 0.022-inch stainless steel archwires, then teeth were moved to a curve of Spee maloclussion with the first premolar intruded 1.5 mm and the canine and second premolar intruded 0.75 mm. Two-way mixed multivariate analysis of variance (α = 0.05) was used to compare forces and moments generated among the three archwires. RESULTS: Statistical differences were found in 55 of 63 comparisons of forces and moments between archwire types for each tooth around the arch. The lowest force magnitudes were measured for labial straight archwires at each tooth position. The lateral incisor experienced the largest gingival forces with all archwire forms. The first premolar and first molar experienced labial-lingual crown tipping moments in opposite directions between labial and the two lingual archwire forms. CONCLUSIONS: Biomechanical differences between labial, lingual straight, and lingual mushroom treatment modalities for the correction of curve of Spee misalignments were elucidated. Labial straight archwire exerted the lowest force magnitudes overall. For both lingual archwire forms, the labial-lingual inclination of the first premolar could be highly variable during levelling.
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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".