Evaluation of the effect of different core substrates on the accuracy of intraoral scanners
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine if different types of core substrates have any effect on the trueness and precision of digital intraoral impressions. MATERIAL AND METHODS: A customized typodont with four similar cores of natural dentine, composite, metal (Ni-Cr), and zirconia in the position of premolars was fabricated. The study model was scanned five times with two types of intraoral scanners (Carestream 3600 and 3Shape Trios 3), and a reference standard scan was obtained using a laboratory scanner (3shape D1000). A metrology software (Geomagic X) was used to align the data of experimental scans and the reference scan to determine deviation values (trueness). Precision values were calculated with random superimposition in each intraoral scanner group. The Kruskal-Wallis test was used to compare differences between different substrates, and the Mann-Whitney test was used to compare the average values between the two scanners. RESULTS: Trios 3 was found to be significantly truer and more precise than Carestream 3600 (p value = .005, <0.001). There were no significant differences in the trueness of different substrates when they were scanned by Trios 3, while different materials showed significantly different trueness values in the Carestream 3600 group (p value = .003). Dentin showed the best trueness, and zirconia performed worse than other substrates. Regarding the precision of the scanners, neither of the scanners was affected by the type of scanning substrate. CONCLUSION: For Carestream 3600, substrate type did impact the trueness of intraoral scans, with dentin and zirconia showing the highest and lowest accuracy, respectively, while Trios 3 was similarly accurate across all substrates. Trios 3 had both higher trueness and precision than Carestream 3600.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".