The effect of scanner type and scan body position on the accuracy of complete‐arch digital implant scans
Bibliographic record
Abstract
BACKGROUND: How the accuracy of complete-arch implant scans is affected when different intraoral scanners (IOSs) are used and the effect of scan body position on the accuracy are not well-known. PURPOSE: To compare the scan accuracy (trueness and precision) of a recently introduced IOS (Virtuo Vivo) to a commonly used IOS (TRIOS 3) and the scans of a laboratory scanner (LBS; Cares 7 SERIES) in a completely edentulous maxilla with four implants. It was also aimed to evaluate the effect of scan body position on the accuracy. MATERIALS AND METHODS: Multi-unit scan bodies were tightened on a poly(methyl methacrylate) edentulous maxillary model with four implants. A master reference model (MRM) stereolithography (STL) file was generated by scanning the model with a high-precision scanner. The model was scanned with three different scanners (n = 10); two different IOSs and a LBS. STL files were superimposed over the MRM. RESULTS: For trueness, scan body position (P = .004) and scanner type (P < .001) had a significant effect on distance deviation and a significant interaction was found (P = .001). For angular deviation, only scanner type had a significant effect (P = .028). For precision, significant difference was found for distance (P = .011) and angular deviations (P = .020) between different scanner types. CONCLUSIONS: One scanner type was not superior to others when both trueness and precision were considered. Position of the scan body affected the distance deviation (trueness).
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".