Clear aligner orthodontic treatment: An international modified Delphi consensus study
Bibliographic record
Abstract
INTRODUCTION: This study aimed to establish a consensus, thanks to the participation of a large group of experts in the field of aligner therapy, on several of its clinical and extraclinical aspects, with particular reference to its potential and biomechanical limitations. METHODS: A Delphi study was conducted in 3 rounds. On the basis of the most recent systematic reviews in the literature, the steering committee formulated 35 questions. A group of 36 international experts agreed to participate in the survey and were asked to respond to the questions, choosing their level of agreement on a scale of 1-5 in the first round, then from 1 to 3 in the second, and finally with a yes or no response in the third, progressively narrowing the field of research. The items for which consensus (≥70%) was obtained were accepted; the others were reformulated. RESULTS: On the basis of the analysis of the experts' responses, 68 questions were reformulated for the second round and 28 for the third round. After 3 rounds, the study generated 47 consensus statements regarding biomechanical aspects and extraclinical factors. CONCLUSIONS: The study, based on the modified Delphi method, collected the opinion of experts, comparing it with the scientific literature to evaluate the potential and limitations of orthodontic aligners, obtaining 47 consensus statements related to biomechanics and extraclinical factors.
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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 | 0.173 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".