An Innovative 3D Printed Tooth Reduction Guide for Precise Dental Ceramic Veneers
Bibliographic record
Abstract
Tooth reduction guides allow clinicians to obtain the ideal space required for ceramic restorations. This case report describes a novel design (CAD) for an additive computer-aided manufactured (a-CAM) tooth reduction guide with channels that permitted access for the preparation and evaluation of the reduction with the same guide. The guide features innovative vertical and horizontal channels that permit comprehensive access for preparation and evaluation of the reduction with a periodontal probe, ensuring uniform tooth reduction and avoiding overpreparation. This approach was successfully applied to a female patient with non-carious lesions and white spot lesions, resulting in minimally invasive tooth preparations and hand-crafted laminate veneer restorations that met the patient's aesthetic demands while preserving tooth structure. Compared to traditional silicone reduction guides, this novel design offers greater flexibility, enabling clinicians to evaluate tooth reduction in all directions and providing a more comprehensive assessment. Overall, this 3D printed tooth reduction guide represents a significant advancement in dental restoration technology, offering clinicians a useful tool for achieving optimal outcomes with minimal tooth reduction. Future work is warranted to compare tooth reductions and preparation time for this guide to other 3D printed guides.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".