Impact of clear aligner therapy on tooth pain and masticatory muscle soreness
Bibliographic record
Abstract
BACKGROUND: Clinical findings suggest that orthodontic treatment with clear aligners (clear aligner therapy/CAT) may cause masticatory muscle soreness in some patients. OBJECTIVE: This multi-site prospective study investigated tooth pain and masticatory muscle soreness and tenderness in patients undergoing CAT and explored whether psychological traits affected these outcomes. METHODS: Twenty-seven adults (22F, 5M; mean age ± SD=35.3 ± 17.6 years) about to start CAT were recruited at three clinics. During CAT, they reported on 100-mm visual analogue scales their tooth pain, masticatory muscle soreness and stress three times per day over 4 weeks (week 1 = baseline; week 2 = dummy aligner; week 3 = first active aligner; week 4 = second active aligner). Pressure pain thresholds (PPTs) were measured at the masseter and temporalis at baseline and after week 4. Mixed models were used to evaluate the outcome measures over time. RESULTS: Clear aligner therapy caused mild tooth pain, which was greater with the passive than the first and second active aligners (both P < .001). Mild and clinically not relevant masticatory muscle soreness was produced by all aligners (all P < .05), with the first active aligner producing less soreness than the dummy aligner (P < .001). PPTs did not change significantly after 4 weeks. Both tooth pain and masticatory muscle soreness were affected by stress and trait anxiety, whilst muscle soreness was affected also by oral behaviours. CONCLUSIONS: In the short term, CAT produces tooth pain and masticatory muscle soreness of limited significance. Frequent oral behaviours are related to increased masticatory muscle soreness during CAT. The medium- and long-term effects of CAT should be further explored.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".