The Effect of Angulation and Scan Body Position on Scans for Implant‐Treated Edentulism: A Clinical Simulation Study
Bibliographic record
Abstract
INTRODUCTION: The acquisition of digital impressions has become an integral part of clinical dentistry. The purpose of the present clinical simulation study was to evaluate the accuracy of digital impressions for maxillary full-arch implant-supported prostheses using two modern intraoral scanners with different acquisition technologies. MATERIAL AND METHODS: Two models of edentulous maxilla, with six implants at positions #16,14,12,22,24,26 (FDI World Dental Federation System, ISO 3950) or #3,5,7,10,12,14 (Universal Numbering system) were digitally designed, and 3D-printed in resin material (Asiga DentaMODEL, Australia). In the first scenario, all implants were parallelized, while in the second, implants #12/#7 and #22/#10 had a 20° angulation buccally, while implants #16/#3 and #26/#14 20° angulation distally. The models were scanned with two different intraoral scanners, Trios3 (3Shape, Denmark) and CS3600 (Carestream Dental, USA). Linear (x, y, z axes-top point) and angular deviations (x, y, z axes-Δφ) were assessed. Statistical analysis was performed using Kolmogorov-Smirnov tests (significance at p < 0.05). RESULTS: Implant angulation showed a significant impact on accuracy, while the two scanners showed statistically significant differences. CS3600 demonstrated superior trueness, while Trios3 superior precision in both clinical scenarios. In the first clinical scenario a predominant occurrence of angular deviations was observed, while in the second scenario both angular and linear deviations were recorded. Scan body position also influenced scanning outcomes, with the last scan body captured demonstrating higher deviations. CONCLUSION: Both scanners provided acceptable accuracy in the acquisition of digital impressions. Implant angulation and scan body position significantly affected trueness and precision. Clinicians should carefully consider implant angulations in full-arch implant restorations, as well as the scanning protocol.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".