Histological assessment of mandibular bone tissue after guided bone regeneration with customized computer‐aided design/computer‐assisted manufacture titanium mesh in humans: A cohort study
Bibliographic record
Abstract
BACKGROUND: Innovative customized computer-aided design/computer-assisted manufacture (CAD-CAM) titanium meshes have been proposed for guided alveolar bone regeneration. Histological confirmation on the quality of the regenerated bone is needed. Purpose of the study is to assess the integration capabilities of these innovative meshes and to evaluate the histological features of the regenerated alveolar bone. MATERIALS AND METHODS: Twenty partially edentulous patients, with severe posterior mandibular atrophy, underwent a guided bone regeneration technique by means of customized CAD-CAM titanium mesh in association with a mixture of autologous bone in chips and deproteinized bovine bone (1:1). At 9 months of healing, titanium meshes and bone samples were collected and histomorphometrically analyzed. RESULTS: In all patients, implants were placed according to the original plan. At histologic analysis, mesh appeared well osseointegrated, except that in sites where membrane exposure occurred. In all sites, newly formed tissue resulted highly mineralized, well-organized, and formed by 35.88% of new lamellar bone, 16.42% of woven bone, 10.88% of osteoid matrix, 14.10% of grafted remnants, and 22.72% of medullary spaces. Blood vessels were the 4% of the tissue. CONCLUSIONS: Data from this study support the use of customized CAD/CAM titanium mesh for regeneration of vital, well-structured, and vascularized alveolar bone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".