Accuracy of intraoral scan images in full arch with orthodontic brackets: a retrospective in vivo study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this retrospective study was to evaluate the accuracy of intraoral scan (IOS) images in the maxillary and mandibular arches with orthodontic brackets. MATERIAL AND METHODS: From digital impressions of 140 patients who underwent orthodontic treatment, consecutive IOS images were selected based on standardized inclusion criteria: Two pre-orthodontic IOS images (IOS1 and IOS2) of permanent dentition with fully erupted second molars and IOS images obtained immediately after orthodontic bracket bonding (IOSb). Superimpositions were performed to evaluate the reproducibility of repeated IOS images. Accuracy of IOSb images was analyzed by comparing the average surface errors between IOS1c and IOS2c images, which were IOS images cut based on the same region of the interest as between IOS1 and IOSb images. RESULTS: A total of 84 IOS images was analyzed. The average surface errors between IOS1 and IOS2 images were 57 ± 8 μm and 59 ± 14 μm in the maxillary and mandibular arch, respectively, and their reliability was almost perfect. The average errors between IOSb and IOS1c images exhibited an increase, which measured 97 ± 28 μm in the maxillary arch and 95 ± 29 μm in the mandibular arch. These surface deviations between IOSb and IOS1c images were significantly larger in each region as well as entire dentition (P < 0.001) compared to those between IOS1c and IOS2c images. CONCLUSIONS: The average surface errors of the scans with brackets showed increased values compared with those without brackets. This suggests that orthodontic brackets could affect the trueness of intraoral scan images. CLINICAL RELEVANCE: It is necessary for clinicians to consider the effect of brackets on digital impression when using IOS images in orthodontic patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".