Accuracy of Dental Implant Placement Using <scp>CBCT</scp>‐Derived Mucosa‐Supported Stereolithographic Template
Bibliographic record
Abstract
PURPOSE: The aim of the present in vivo study was to evaluate whether a difference exists between the maxilla and the mandible regarding the precision of implant placement utilizing a cone beam computed tomography (CBCT)-derived mucosa-supported stereolithographic (SLA) template. MATERIALS AND METHODS: Eighty implants (44 maxilla, 36 mandible) were placed in 18 fully edentulous jaws (10 maxillas, eight mandibles) using a mucosa-supported SLA surgical template. A voxel-based registration technique was applied to match the postoperative and preoperative CBCT scans. RESULTS: Vertical deviation (p = .026) at the implant hex and angular deviation (p = .0188) were significantly lower in the maxilla than in the mandible. The global linear deviation and lateral deviation at the implant hex were not significantly different. At the implant apex, the average maximum vertical deviation was within 1 mm (0.1-4.6 mm). The average maximum lateral deviation was 1.8 mm (0.9-5.5 mm) in the maxilla and 2.3 mm (0.5-5.5 mm) in the mandible when a 15-mm-long implant was placed. CONCLUSIONS: When using CBCT-derived mucosa-supported SLA templates, clinicians should be aware of differences in the angular deviation of the implants in the mandible and maxilla. The average maximum linear deviation should be considered as a safety margin at the implant apex.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".