External Root Resorption after Orthodontic Treatment with Invisalign®: A Retrospective Study
Bibliographic record
Abstract
AIM: To measure the incidence and severity of root resorption after orthodontic treatment with Invisalign. MATERIAL AND METHODS: This retrospective study was conducted at Riyadh, Saudi Arabia from June 2017 to January 2018. Pre- treatment and post-treatment Orthopantographs were obtained from orthodontic records of 29 patients managed with aligners (Invisalign®, Align Technologies, Santa Clara, CA, USA) at different dental clinics in Riyadh City. The selected sample was fulfilled the following criteria: (1) Class I malocclusion, (2) Mild to moderate crowding, (3) Non-extraction orthodontic treatment, (3) No evidence of root resorption before orthodontic treatment, (4) No root abnormalities or dilaceration, and (5) Good quality of pre- and post-treatment Orthopantographs. One examiner performed the measurements directly on the Orthopantographs using electronic digital caliper (Mitutoyo Manufacturing Co. Ltd., Tokyo, Japan) with an accuracy of 0.01mm. The measurements were performed on maxillary and mandibular central incisors, lateral incisors, and canines pre- and post-operatively, resulting in a total of 696 measurements. The crown length was measured from incisal edge to cemento- enamel-junction, while the root length from cemento-enamel-junction to root apex. RESULTS: In our study, 72% of the teeth demonstrated root resorption, in regard to the severity of root resorption, we found that mild root resorption > 0% up to 2% in all the affected teeth. Upper Anterior teeth have more significant resorption rate than lower anterior teeth P<0.05. CONCLUSION: The present study showed that incidence of root resorption was high after orthodontic treatment with Invisalign®, however the severity is very low and it is limited to the surface resorption only.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".