New Fabrication System for Dental Implant Surgical Stents: Time‐Saving Laboratory Technique Using a Light‐Curing Resin and Transparent Artificial Teeth
Bibliographic record
Abstract
BACKGROUND: Surgical stents are a prerequisite for dental implant diagnosis. However, the traditional fabrication method including wax-up, investment, and resin polymerization is time consuming. PURPOSE: This article introduces a new fabrication system using a light-curing resin and transparent artificial teeth to reduce time spent in the laboratory. MATERIALS AND METHOD: The process of this system includes placing the light-curing resin and the artificial teeth on the partially edentulous dentition and alveolar ridge, making an appropriate pattern for the stent, light-curing for 5 minutes, briefly polishing, and making holes for titanium guide pins. RESULTS: This system requires only 30 to 40 minutes in the laboratory to complete a stent after mounting the casts on the articulator. Owing to the elasticity of the light-curing resin, this system eliminates the need for a blockout procedure on the undercuts of existing teeth on the casts, protects the casts from breakage, and provides the appropriate retention intraorally without any retainers. As well, titanium guide pins embedded in the stent were clearly identified by panoramic and computed tomographies. CONCLUSIONS: The new fabrication system proposed here can be time saving and, further, serve benefits for radiographic diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.002 |
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".