Tracking orthodontic tooth movement and associated biomechanics using an integrated clinical and in vitro mechanical approach
Bibliographic record
Abstract
The objective of this study was to establish an integrated clinical and in vitro experimental approach to track tooth positions and replicate digital tooth positions in vitro for biomechanical load measurement over orthodontic treatment. Patients between 11-14years were recruited to collect four digital intraoral scans in 4-6-week intervals. Patients were treated for mild anterior crowding using 0.022’’ Damon Q2 brackets and CuNiTi round archwires sized up at each treatment interval (T1-T2: 0.014’’, T2-T3: 0.016’’, T3-T4: 0.018’’). Scans were superimposed and clinical tooth movement was tracked using bracket-slot midpoint position differences. An in-house workflow was developed using MATLAB and SolidWorks to replicate digital bracket positions on an Orthodontic Simulator (OSIM) with custom-dimensioned jigs. Mechanical experiments for the sample arches were performed at 37°C for 3D force measurements at each tooth upon wire insertion (n=5/archwire size). The average superimposition error between T2-T4 and T1 scans was 0.19mm. Average errors in bracket position replication across all directions was 0.41mm in the local X-, Y-, and Z-direction, respectively. The initial force and tooth movement range was 0.00-1.43N and 0.01-1.81mm in the Y-direction, and 0.01-2.17N and 0.00-1.45mm in the Z-direction. Tooth movement ranged from 0.00-0.30 mm/week in the Y-direction and 0.00-0.24 mm/week in the Z-direction over treatment. This study developed a process to measure clinical tooth movement and existing force/moment systems for sample arches over orthodontic braces treatment. Future work will involve an expanded data set to establish fundamental relationships between force systems and clinical tooth movements.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".