Lateral bone augmentation with a composite graft covered with a stretched and pinned collagen membrane: A retrospective case series using cone‐beam computed tomography
Bibliographic record
Abstract
AIMS: (1) To assess the effectiveness of the Sausage Technique™ when applied for lateral bone augmentation by multiple experienced clinicians; (2) To identify risk indicators for a poor outcome and to assess the need for adjunctive surgery. MATERIALS AND METHODS: All patients who had been treated with the Sausage Technique™ for lateral bone augmentation by three experienced surgeons between January 2019 and December 2021 were included in a retrospective case series. The Sausage Technique™ technique includes the use of autogenous bone chips and deproteinized bovine bone mineral (1:1 ratio), covered with a stretched and pinned collagen membrane. The increase in alveolar width between the pre-operative situation and 9 months was assessed at different levels on superimposed cone-beam CT scans. RESULTS: Twenty-five augmentations performed in 25 patients (17 males, 8 females, mean age 51 years) were available for evaluation. Mean alveolar width increased from 4.35 to 7.43 mm at 3 mm below the crest. The mean increase of 3.08 mm (95% CI 2.10-4.06; p < 0.001) was significant. The outcome of non-containing single implant sites was significantly worse than the outcome of other sites (MD 2.67 mm; p = 0.008). The need for regrafting was 4% and the need for soft tissue augmentation was 48%. Twenty percent of the patients needed soft tissue augmentation due to a lack of keratinized mucosa width, and 32% due to a lack of buccal convexity. The former was mainly needed at multiple implant sites, whereas the latter was mainly required at single implant sites. All implant survived and remained healthy until the final follow-up. CONCLUSION: The Sausage Technique™ is an effective bone augmentation technique. Non-containing single implant sites were associated with a poor outcome and adjunctive soft tissue augmentation was needed in about half of the patients.
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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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".