Do Alveolar Bone Dehiscences and Fenestrations Remodel After Adult Non‐Extraction Clear Aligner Therapy? A Retrospective Study up to 2 Years in Retention
Bibliographic record
Abstract
OBJECTIVE: To evaluate the radiographic presence and magnitude of alveolar bone dehiscences (ABDs) and fenestrations (ABFs) in maxillary and mandibular anterior teeth of adults with dental Class II malocclusion, before (T1), immediately after (T2), and up to 2 years after (T3) non-extraction clear aligner therapy (CAT). SETTING AND SAMPLE POPULATION: Records from 14 adults with dental Class II malocclusion treated with non-extraction CAT and Class II elastics were retrospectively obtained. MATERIALS AND METHODS: A total of 332 labial and lingual anterior root surfaces were assessed using cone beam computed tomography (CBCT) at T1, T2 and T3. Dehiscences were recorded when the linear measurement for dehiscence (LM-D) was more than 2 mm from the cementoenamel junction. The defect was classified as ABF when it did not involve the alveolar crest and the linear measurement for fenestration (LM-F) measured more than 2.2 mm. Changes in incisor inclination and intercanine width were calculated. Binary logistic regression analyses were used to assess the association between CAT and the presence of ABDs/ABFs. Linear regression analyses were used to identify factors affecting the magnitude of LM-Ds/LM-Fs. RESULTS: Non-extraction CAT was associated with an increased presence of ABDs at T2 compared to T1 [Odds Ratio (OR): 2.69; 95% Confidence Interval (CI): 1.92-3.76]. The association remained significant at T3 (OR: 2.46, 95% CI: 1.76-3.45). Non-extraction CAT was not significantly associated with the presence of ABFs at T2 and T3. CONCLUSIONS: Alveolar bone remodelling during retention did not result in the reduction or resolution of post-treatment radiographic alveolar bone defects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".