Vertical ridge augmentation feasibility using unfixed collagen membranes and particulate bone substitutes: A 1‐ to 7‐year retrospective single‐cohort observational study
Bibliographic record
Abstract
AIM: To determine whether vertical ridge augmentation (VRA) can be obtained through guided bone regeneration (GBR) using exclusively resorbable collagen membranes and particulate bone substitutes without additional stabilization. MATERIALS AND METHODS: This study retrospectively examined 22 participants who underwent VRA with staged or simultaneous implant placement. The vertical defects of all participants were filled with particulate bone substitutes and covered with resorbable collagen membranes. The augmented sites were stabilized with unfixed collagen membranes and the flap without any additional fixation. The augmented tissue height was assessed using cone-beam computed tomography at baseline, immediately after surgery, and at annual follow-ups. RESULTS: The vertical bone gain of the 22 augmented sites amounted to 6.48 ± 2.19 mm (mean ± SD) immediately after surgery and 5.78 ± 1.72 mm at 1- to 7-year follow-up. Of the 22 augmented sites, 18 exhibited changes of less than 1 mm, while the other 4 showed changes of greater than 1 mm. Histological observation of three representative cases revealed new bone apposition on the remaining material. CONCLUSION: The present findings indicate that GBR procedures using exclusively collagen membranes and particulate biomaterials without any additional fixation are feasible options for VRA.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".