Effect of Barrier Membranes and Autologous Bone Grafts on Ridge Width Preservation Around Implants
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to explore the effect of barrier membranes and autologous bone grafts on the preservation of ridge width around implants. MATERIALS AND METHODS: Sixty-one patients were chosen from a database of case series; treatment assignment was based on clinical judgment. The patients received a total of 76 implants: 34 implants with guided tissue regeneration procedures, 27 with autologous bone grafts, and 15 without ridge preservation procedures (control group). RESULTS: The ridge width around the 76 implants decreased an average of 1.4 mm (95% CI, -1.9 to -1.1 mm). Implants placed with autologous bone grafts or membranes tended to have worse outcomes. When compared to the no augmentation group, implants placed with guided tissue regeneration and autologous bone grafts resulted in 0.1 mm of additional bone loss (95% CI, -1.7 to -1.5 mm; p < .93) and 0.8 mm of additional bone loss (95% CI, -2.3 to 0.7 mm; p < .28), respectively. CONCLUSIONS: Autologous graft procedures or barrier membranes do not appear to improve crestal ridge preservation around implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".