A novel digital and visualized guided bone regeneration procedure and digital precise bone augmentation: A case series
Bibliographic record
Abstract
Abstract Background Although the traditional bone augmentation technology can basically meet the clinical needs at present, the effect of bone augmentation in most cases is related to the experience of the operator. Propose This study commits to providing a digital solution for precise bone augmentation in the field of oral implantology. Materials and methods After collecting the data of patients' intraoral scanning and DICOM (digital imaging and communications in medicine), the implant position is digitally designed, and the alveolar bone is digitally augmented around the ideal implant position. On the premise of ensuring that the thickness of labial bone is 2 mm, and there is sufficient alveolar bone 3 to 4 mm apically from the ideal gingival margin for implant placing, we carry out excessive augmentation of 0.5 and 1 mm on the labial bone and alveolar crest, respectively, to compensate for possible bone resorption after 6 months. After 3D printing the reconstructed alveolar bone model, the titanium mesh is trimmed and preformed on the alveolar bone model. Outcomes are reported in terms of mean values (5%‐95% percentile values). Results Thirty implant sites have accepted this novel virtually designed alveolar bone augmentation. Before the second‐stage surgery, the average vertical bone gain was 2.48 mm (0.29‐6.32), the average horizontal bone gain was 4.11 mm (1.19‐8.74), the average height of the residual alveolar bone above the implant platform was 1.44 mm (0.59‐2.92), the average thickness of the labial bone width at the implant platform was 2.00 mm (0.93‐3.64), the average thickness of the labial bone width at 2 mm apically from the implant platform was 2.74 mm (1.40‐5.46). The virtual augmentation of each tooth position was 349.41 mm3 (165.70‐482.70), while the actual augmentation of each tooth position was 352.94 mm3 (159.24‐501.78), the accuracy of the final actual augmentation reached 95.82% (range from 88.53% to 99.15%). Conclusion This case series suggests that a virtually digital guided bone regeneration (GBR) workflow is precise and controllable. The practicality, safety and effectiveness of this procedure needs to be compared to other bone augmentation procedures in randomized controlled trials.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".