Clear aligner therapy practices among orthodontists practicing in Canada
Bibliographic record
Abstract
BACKGROUND: The acceptability and preference for clear aligner therapy (CAT) has been increasing among orthodontists, but there is still a lack of consensus regarding CAT best practices. Consequently, this study aimed to investigate CAT practices among orthodontists practicing in Canada. METHODS: The survey was conducted among orthodontists practicing in Canada using a modified previously published survey. Sixty orthodontists participated (6.1% response rate). It consisted of 11 sections with open and closed questions related to demographic information and particularities about using or not using CAT. The survey responses were exported from REDCap to a Microsoft Excel (Microsoft, Redmond, Wash) spreadsheet, then statistically analyzed using SPSS software (SPSS for Windows, version 21.0; IBM Inc., Armonk, NY, USA). The comments were categorized under themes and subthemes. Data were organized in descriptive statistics, expressing frequencies and percentages. RESULTS: Almost 30% of the orthodontist's annual caseload was treated with CAT, most frequently prescribed to adult patients. Case complexity and patient cooperation were the factors that most influenced the decision to prescribe CAT. Almost half of orthodontists reported sometimes combining CAT with adjunctive fixed appliances. CONCLUSIONS: Most orthodontists prescribe CAT, and its use is based on the malocclusion's complexity. Orthodontists who do not prescribe CAT believe that fixed appliance therapy has superior treatment outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".