An evaluation of root resorption associated with the use of photobiomodulation during orthodontic treatment with clear aligners: a retrospective cohort pilot study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the change in tooth root volume using cone-beam computed tomography (CBCT) in a group of patients treated concurrently with clear aligners and an adjunctive photobiomodulation (PBM) device. MATERIALS AND METHODS: This retrospective cohort pilot study included the records of 32 consecutively treated clear aligner patients (23 female, 9 male) from the private practice of one orthodontist. The PBM group (n = 16) used the device once per day for 5 minutes per arch and was compared with a matched control group (n = 16). A semiautomated segmentation technique was used to obtain tooth volume of anterior teeth from CBCT imaging prior to (T0) and during or immediately following (T1) orthodontic treatment with clear aligners. The change in root volume between time points was assessed. RESULTS: There was no statistically significant difference between the pre- and posttreatment root volumes of maxillary and mandibular anterior teeth, regardless of which intervention group the patient belonged to (P > .05). There was also no difference in the mean percentage change in root volume between clear aligner patients in this study who were treated with the PBM device compared with a matched control group (P > .05). CONCLUSIONS: Clear aligner patients in this study who changed their aligners every 3 to 5 days and used adjunctive photobiomodulation therapy did not experience clinically relevant orthodontically induced external root resorption. Due to the small sample size and measurement error in the root segmentation process, the results should be interpreted with caution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".