Insight into clear aligner therapy protocols and preferences among members of the American Association of Orthodontists in the United States and Canada
Bibliographic record
Abstract
OBJECTIVES: To investigate aligner treatment protocols among orthodontists in the United States and Canada and assess the factors influencing clinician choices in aligner systems, treatment protocols, and targeted malocclusions for aligners. MATERIALS AND METHODS: A validated online questionnaire was developed specifically for this research and consisted of three sections. Section 1 evaluated demographics and experience with aligners. Section 2 assessed patient selection and demands and clinician confidence in treating various malocclusions with aligners. Section 3 evaluated treatment protocols used by clinicians. The American Association of Orthodontists Partners in Research Program distributed the survey via e-mail to active members in the United States and Canada. RESULTS: A total of 160 providers completed the survey. Aligners were used by 65.00% of respondents, with the Invisalign system the most popular (81.25%). Aligners were mostly used for adults (97.50%). Tipping was ranked as the easiest movement (1.79 ± 1.35). Extrusion (4.34 ± 1.53) and root movement (4.31 ± 1.27) were ranked as the most difficult. Most were confident treating mild (98.8%) and moderate (82.5%) crowded cases, spacing (96.9%), and anterior crossbite (85%). Of the providers, 58.12% recommended aligners to be changed weekly. Respondents who were confident addressing some of the severe malocclusions were more likely to use Invisalign. CONCLUSIONS: Invisalign is the most popular aligner system, and clinicians seem to be confident using it. Providers are aware of the pitfalls of aligners; they find it challenging to perform root movement and extrusion, and they seem confident treating mild to moderate malocclusions. They avoid complex cases with impactions and severe skeletal problems.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".