Assessment of the application of a novel three‐dimension printing individualized titanium mesh in alveolar bone augmentation: A retrospective study
Bibliographic record
Abstract
OBJECTIVE: To assess the clinical and radiographic outcomes of alveolar ridge augmentation using a novel three-dimensional printed individualized titanium mesh (3D-PITM) for guided bone regeneration (GBR). MATERIALS AND METHODS: Preoperative cone-beam computed tomography (CBCT) was used to evaluate alveolar ridge defects, followed by augmentation with high-porosity 3D-PITM featuring circular and spindle-shaped pores. Postoperative CBCT scans were taken immediately and after 6 months of healing. These scans were compared with preoperative scans to calculate changes in bone volume, height, and width, along with the corresponding resorption rates. A statistical analysis of the results was then conducted. RESULTS: , with a resorption rate of 16.05% ± 8.07%. For 38 implant sites, the average vertical bone increment was 3.63 ± 2.29 mm, with a resorption rate of 17.55% ± 15.10%. The horizontal bone increment at the designed implant platform was 4.43 ± 1.85 mm, with a resorption rate of 25.26% ± 15.73%. The horizontal bone increment 2 mm below the platform was 5.50 ± 2.48 mm, with a resorption rate of 16.03% ± 9.57%. The main complication was exposure to 3D-PITM, which occurred at a rate of 15.79%. CONCLUSION: The novel 3D-PITM used in GBR resulted in predictable bone augmentation. Moderate over-augmentation in the design, proper soft tissue management, and rigorous follow-ups are beneficial for reducing the graft resorption and the incidence of exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".