Deformation of Stereolithographically Produced Surgical Guides: An Observational Case Series Report
Bibliographic record
Abstract
BACKGROUND: Template-based computer-guided implant placement holds the promise of more precise and less traumatic placement of dental implants. Errors in the fabrication process of the surgical guide may lead to unfavorable clinical outcomes. PURPOSE: This report discusses the potential of unintentional volumetric deformation of stereolithographically (SLA) produced surgical guides (NobelGuide, Nobel Biocare AB, Göteborg, Sweden) compared with the original scan denture. MATERIALS AND METHODS: Three-dimensional radiographic data acquired by medical computerized tomography (CT) or cone beam CT (Newtom 3G, AFP Imaging Corporation, Elmsford, NY, USA) can be utilized in specialized software to develop treatment planning for dental implant placement. This information can then be transferred to the patient via a surgical guide. Stereolithography is a rapid prototyping process that can be used to create such a guide stent. Three cases are shown describing different levels of deformation of SLA-produced surgical guides. RESULTS: Unintentional deformation of SLA-produced surgical guides is possible. Deformation of surgical guides can create dissimilarity between the virtually planned position and the actual position of the implants. CONCLUSION: Incorrect setting of the ISO values for the segmentation of the scan denture has been found to be a factor in the deformation of SLA-produced surgical guides, although more, at this time known but also less understood, issues appear to be involved. Only one manufacturer's product is discussed; further research is warranted to determine if the discrepancies are process or product based. It might be prudent to closely evaluate the volumetric congruence of SLA-produced surgical stents before their clinical use to prevent undesired clinical outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".