The Influence of Laboratory Scanner versus Intra-Oral Scanner on Determining Axes and Distances between Three Implants in a Straight Line by Using Two Different Intraoral Scan Bodies: A Pilot In Vitro Study
Bibliographic record
Abstract
Background: The purpose of this in vitro study was to compare the inter-implant distance, inter-implant axis, and intra-implant axis of three implants in a straight line by using a laboratory scanner (LBS) versus an intra-oral scanner (IOS) with two different intra-oral scan bodies (ISBs). Methods: A 3D model was printed with internal hex implant analogs of three implants in positions 15#, 16#, and 17#. Two standard intra-oral scan bodies (ISBs) were used: MIS ISB (two-piece titanium) and Zirkonzhan ISB (two-piece titanium). Both ISBs were scanned using 7 Series dental wings (LBS) and 30 times using Primescan (IOS). For each scan, a stereolithography (STL) file was created and a comparison between all the scans was performed through superimposition of the STL files by using 3D analysis software (PolyWorks® 2020; InnovMetric, Québec, QC, Canada). A Kolmogorov–Smirnov test was performed followed by a Mann–Whitney test (p < 0.05). Results: The change in inter-implant distance for the MIS ISB was significantly lower compared to the ZZ (p < 0.05). The change in intra-implant angle was significantly lower for the ZZ ISB compared to MIS (p < 0.05). The changes in inter-implant angle between the mesial and middle and between the middle and distal were significantly lower for MIS compared to ZZ in contrast to mesial to distal, which was significantly higher (p < 0.05). Conclusions: Both ISBs showed differences in all the parameters between the LBS and the IOS. The geometry of the scan abutment had an impact on the inter-implant distance as the changes in the inter-implant distance were significantly lower for the MIS ISB. The changes in the intra-implant angle were significantly lower for the ZZ ISB. There is a need for further research examining the influence of geometry, material, and scan abutment parts on the 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".