Effect of intraoral scanner and fixed partial denture situation on the scan accuracy of multiple implants: An in vitro study
Bibliographic record
Abstract
BACKGROUND: Accuracy of intraoral implant scans may be affected by the region of the implant and the type of the intraoral scanner (IOSs). However, there is limited knowledge on the scan accuracy of multiple implants placed for an implant-supported fixed partial denture (FPD) in different partially edentulous situations when digitized by using different IOSs. PURPOSE: To investigate the effect of IOS and FPD situation on the scan accuracy of two implants when partial-arch scans were performed. MATERIALS AND METHODS: Tissue level implants were placed in 3 maxillary models with implant spaces either at right first premolar and right first molar sites (Model 1, 3-unit FPD), at right canine and right first molar sites (Model 2, 4-unit FPD), or at lateral incisor sites (Model 3, 4-unit FPD). Reference standard tessellation language (STL) files of the models were generated by using an optical scanner (ATOS Capsule 200MV120). Two IOSs (CEREC Primescan [CP] and TRIOS 3 [TR]) were used to perform partial-arch scans (test-scans) of each model (n = 14), which were exported in STL format. A metrology-grade analysis software (GOM Inspect 2018) was used to superimpose test-scan STLs over the reference STL to calculate 3D distance, inter-implant distance, and angular (mesiodistal and buccopalatal) deviations. Trueness and precision analyses were performed by using bootstrap analysis of variance followed by Welch tests with Holm correction (α = 0.05). RESULTS: Trueness of the scans was affected by IOS and FPD situation when 3D distance deviations were considered, while inter-implant distance, mesiodistal angular, and buccopalatal angular deviations were only affected by the FPD situation (p < 0.001). Scan precision was affected by the interaction between the IOSs and the FPD situation when 3D distance and buccopalatal angular deviations were concerned, while IOSs and FPD situation were effective when all deviations were concerned (p≤ 0.001). When 3D distance deviations were considered, CP scans had higher accuracy TR scans in Models 1 and 3 (p ≤ 0.002), and the Model 1 scans had the highest accuracy (p < 0.001). When inter-implant distance deviations were considered, Model 1 scans had the highest accuracy with CP and higher accuracy than Model 2 when TR was used (p ≤ 0.030). When mesiodistal angular deviations were considered, Model 1 scans had the highest accuracy (p ≤ 0.040). When buccopalatal angular deviations were considered, Model 1 scans had the highest accuracy among models when CP was used (p ≤ 0.020). CONCLUSIONS: Posterior 3-unit fixed partial denture implant scans, CP scans, and combination of these two factors had accuracy either similar to or better than their tested counterparts.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".