Accuracy of Image‐Fusion Stereolithographic Guides: Mapping <scp>CT</scp> Data with Three‐Dimensional Optical Surface Scanning
Bibliographic record
Abstract
BACKGROUND: Computer-assisted implant surgery usually requires a radiographic scan template as the basis for prosthetic-driven implant planning and surgical guide fabrication. PURPOSE: The study aims to evaluate the accuracy of image-fusion stereolithographic guides in which a computed tomography (CT) scan is mapped with three-dimensional optical scans of cast and diagnostic wax-up. MATERIALS AND METHODS: Three-dimensional error at the base and tip of the implants, angular deviation of the implant-axis, and the inserting-depth error of 120 implants with a length of 10 mm and a caliber of 4.1 mm in 15 polymer-models were examined. A control CT was performed and fused with the planning data for accuracy evaluation. RESULTS: The mean three-dimensional error was 0.21 ± 0.10 mm (0.00-0.48 mm) at the implant base, 0.32 ± 0.17 mm (0.03-0.75 mm) at the implant tip, and the mean angular error was 0.85 ± 0.59° (0.00-2.50°). The mean depth error was 0.07 ± 0.07 mm (0.00-0.32 mm). CONCLUSIONS: Within the limitations of an in vitro study, the novel technique showed excellent accuracy. Errors from fabrication and scanning of a radiographic scan template can be avoided, and the workflow and costs of computer-assisted implant surgery may be reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".