Micro-computed tomography assessment of regional and overall accuracy of thermoformed retainers and intraoral scanners
Bibliographic record
Abstract
INTRODUCTION: This retrospective in vitro study aimed to assess the accuracy of retainers fabricated using Essix (Dentsply Sirona, Charlotte, NC) and Zendura (Zendura Dental, Fremont, Calif), and to compare the accuracy of intraoral scanners iTero (Align Technology, San Jose, Calif) and TRIOS (3Shape, Copenhagen, Denmark). In addition, regional accuracy across different areas of the mandibular arch was analyzed. METHODS: A total of 20 standard tessellation language files from postorthodontic treatment mandibular arches (from January 2019 to August 2024) were selected based on specific inclusion criteria. The standard tessellation language files were 3-dimensional printed and scanned using iTero Element 2 and TRIOS 4, then used to fabricate 20 sets of each thermoformed retainer (Zendura and Essix). All models were scanned using micro-computed tomography (Scanco Medical AG, Brüttisellen, Switzerland), serving as the gold standard for accuracy comparisons. Root mean square (RMS) error analysis was used to assess overall and regional accuracy. RESULTS: The RMS error between gold standard and retainers differed significantly overall (P = 0.044), particularly in the anterior (P = 0.030) and premolar (P = 0.017) regions, with greater discrepancies in Zendura retainers. RMS error differences were not significant between intraoral scanners across most regions, except for borderline significance in the anterior region (P = 0.058), in which TRIOS showed larger deviations. CONCLUSIONS: Both intraoral scanners demonstrated comparable accuracy. However, Zendura retainers exhibited greater inaccuracies than Essix. Regional analysis showed higher deviations in the molar and lingual regions for scanners and the molar regions for retainers. Importantly, these discrepancies were low and clinically insignificant.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".