Lateral bone augmentation in narrow posterior mandibles, description of a novel approach, and analysis of results
Bibliographic record
Abstract
BACKGROUND: Combination of particulate grafts and collagen membranes is widely used for augmentation of bony defects for implant placement. Fixation of the barrier membrane may avoid complications due to unfavorable mechanical properties and poor stability leading to collapse of the augmented area. PURPOSE: To evaluate a new simplified method for resorbable collagen membrane fixation in lateral bone augmentation procedures in narrow posterior mandibles. MATERIALS AND METHODS: This retrospective study analyzed 16 procedures performed in 15 patients who followed lateral ridge augmentation procedures before implant placement in the posterior mandible. A particulate mineralized bone allograft was covered with a cross-linked resorbable collagen barrier membrane, which was fixated with a single, nonresorbable pin. Complications were registered and results analyzed on pre and post op measurements on computerized tomographic scans. Descriptive statistical analysis and ANOVA with repeated measures were performed. RESULTS: No complications were recorded. Average bone gain was 3.3 mm at implant platform level and 4.29 mm at 3 mm apically, both, statistically significant. All sites had sufficient bone width allowing implant placement. Thirty-three implants placed in the augmented areas, integrated and survived for over a 2-year follow-up. CONCLUSION: The simplified membrane fixation procedure enables large horizontal bone gain with minimal complications while allowing adequate implant placement.
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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".