Accuracy of Three‐Dimensional Printed Templates for Guided Implant Placement Based on Matching a Surface Scan with <scp>CBCT</scp>
Bibliographic record
Abstract
BACKGROUND: Reference elements are necessary to transfer a virtual planning into reality for guided implant placement. New systems allow matching optical scans with three-dimensional radiographic images. PURPOSE: To test whether digitally designed three-dimensional printed templates (D-temp) fabricated by matching surface scans and cone beam computed tomography (CBCT) images differ from the templates fabricated in-lab (L-temp) by using a physical transfer device for the positioning of the guiding sleeves. MATERIALS AND METHODS: L-temp were fabricated for eight human lower cadaver-jaws applying a digital planning software program (smop, Swissmeda AG, Zürich, Switzerland) using a Lego® (Lego Group, KIRKBI A/S, Billund, Denmark) brick as reference element and the respective transfer device (X1-table). Additionally, digital templates (D-temp) using the identical planning data sets and software were virtually designed and three-dimensional printed, after matching a surface scan with CBCT data. The accuracy of both templates for each planning was evaluated determining the estimated coronal, apical, and angular deviation if templates were used for implant placement. RESULTS: Mean coronal deviations for L-temp were 0.31 mm (mesial/distal), 0.32 mm (lingual/buccal), and 0.16 mm and 0.23 mm for D-temp, respectively. The mean apical deviations for L-temp were 0.50 mm (mesial/distal), 0.50 mm (lingual/buccal). and 0.25 mm and 0.34 mm for the D-temp, respectively. Differences between both devices were statistically significant (p < .05). CONCLUSIONS: A higher accuracy of implant placement can be achieved by using three-dimensional printed templates produced by matching a surface scan and CBCT as compared with templates which use physical elements transferring the virtual planning into reality.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".