Tip, torque and rotation of maxillary molars during distalization using Invisalign: a CBCT study
Bibliographic record
Abstract
Abstract Background Desirable molar distalization by bodily movement is challenging and can be difficult to achieve. This study investigated changes in molar angulation (mesiodistal tipping), molar inclination (buccolingual torque) and rotation during distalization using clear aligner therapy (CAT). Materials and methods This retrospective study included 38 cone beam computed tomographic images (CBCTs) taken for patients treated with molar distalization using CAT. The study evaluated pre- (T0) and post-treatment (T1) CBCTs of 19 adult patients (36.68 ± 13.50 years) who underwent maxillary molar distalization using Invisalign ® aligners (Align Technology, Inc., San José, CA, USA) with a minimum of 2 mm distalization. Changes in maxillary molar tip, torque and rotation were measured for 61 molars (183 roots). Paired t-test was used to evaluate the differences between pre- and post-treatment readings. The level of significance was set at p ≤ 0.05. The reproducibility of measurements was assessed by the intraclass correlation coefficient (ICC). Results Molar angulation did not show significant change after distalization ( p = 0.158) however, there was significant increase in buccal molar inclination ( p = 0.034) and mesiobuccal molar rotation ( p < 0.001). Conclusion Molar distalization of 2 mm did not cause significant molar tipping. Maxillary molars showed significant buccal inclination (increased torque) and mesiobuccal rotation after distalization.
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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 | 0.001 |
| 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".