Research on the dimensional accuracy of customized bone augmentation combined with <scp>3D</scp>‐printing individualized titanium mesh: A retrospective case series study
Bibliographic record
Abstract
BACKGROUND: Few studies have focused on the dimensional accuracy of customized bone grafting by means of guided bone regeneration (GBR) with 3D-Printed Individual Titanium Mesh (3D-PITM). PURPOSE: Digital technologies were applied to evaluate the dimensional accuracy of customized bone augmentation with 3D-PITM with a two-stage technique. MATERIALS AND METHODS: Sixteen patients were included in this study. The CBCT data of post-GBR (immediate post-GBR) and post-implantation (immediate post-implant placement) were 3D reconstructed and compared with the pre-surgical planned bone augmentation. The dimensional differences were evaluated by superimposition using the Materialize 3-matic software. RESULTS: The superimposition analysis showed that the maximum deviations of contour between were 3.4 mm, and the average differences of the augmentation contour were 0.5 ± 0.4 and 0.6 ± 0.5 mm respectively. The planned volume of bone regeneration was approximately equal to the amount of regenerated bone present 6 to 9 months after the surgical procedure. On average, the vertical gain in bone height was about 0.5 mm less than planned. And, the horizontal bone gain on the straight buccal of the dental implants and 2 to 4 mm apical of the platform fell also about a 0.5 mm short on average. Statistically significant differences were observed between the augmented volume of virtual and post-GBR, and the horizontal bone gain of post-implantation on the level of 4 mm apical to the implant platform (P < .05). CONCLUSIONS: The dimensional accuracy of customized bone augmentation with the 3D-PITM approach needs further improvement and compared to other surgical approaches of bone augmentation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".