The effect of scanning the palate and scan body position on the accuracy of complete‐arch implant scans
Bibliographic record
Abstract
BACKGROUND: Whether stitching the palate during intraoral digital scans of implants would improve, scanning accuracy is unclear. PURPOSE: Evaluate the effect of stitching the palate and the scan body position on the trueness (distance and angular deviation) and precision of digital scans in a completely edentulous situation. MATERIALS AND METHODS: An edentulous maxillary model with four parallel dental implant analogs was fabricated and intraoral scan bodies were attached. The entire surface was scanned using an industrial scanner to generate a master reference model digital scan (MRM-DS). Digital scans of the master model were made using an intraoral scanner and the resulting scans were divided into two groups [stitched palate (S) and unstitched palate (U)]. All test scans were converted to STL files and superimposed over the MRM-DS. RESULTS: For trueness, scan body position had a significant effect on distance (P < .001) and angular (P < .001) deviation values. In terms of precision, no significant difference was found in distance (P = .051) and angular deviations (P = .36) between stitched and unstitched techniques. CONCLUSIONS: The accuracy and precision of digital scans of edentulous maxillary arch was similar independent of stitching or unstitching the palate. Position of the implant had a significant effect on 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".