Horizontal guided bone regeneration on knife‐edge ridges: A retrospective case–control pilot study comparing two surgical techniques
Bibliographic record
Abstract
INTRODUCTION: Studies evaluating guided bone regeneration (GBR) on knife-edge ridges using absorbable membranes with staged approaches have reported various horizontal bone gains. This study compared the horizontal bone gain obtained via a conventional technique of GBR and a recently-reported technique. Bone loss during the healing process was also measured. METHODS: Consecutive patients who underwent GBR on knife-edge ridges via a conventional technique (control group) or the Sausage Technique (test group) were included in this study. GBR was performed using a collagen membrane and deproteinized bovine bone mineral combined with an autogenous graft at a 1:1 ratio. Cone-beam computed tomography (CBCT) was performed preoperatively, postoperatively, and after the patient healed. Horizontal bone width was measured on CBCT images 2 mm apical from the top of the crest. The preoperative CBCT and posthealing CBCT were superimposed to calculate the bone gain after healing, and the preoperative and postoperative CBCT scans were superimposed to calculate the bone gain after surgery. Bone loss during healing was calculated by subtracting the width of the ridge after healing from the postoperative width. RESULTS: The mean horizontal bone gain was significantly lower in the control group (2.7 ± 1.8 mm; 83.2%) than in the test group (5.3 ± 2.3 mm; 216.8%) (p = 0.003). The average horizontal bone loss between regeneration and implant placement was 0.9 mm in the control group (27.9%) and 2.1 mm in the test group (29.4%). While the absolute bone loss was significantly different (p = 0.012), the percentage of bone resorption was not (p = 0.608). CONCLUSION: The new technique resulted in significantly more bone gain than a conventional GBR technique. The rate of graft resorption during healing was stable regardless of the amount of grafted material.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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".