Using Cone Beam Computed Tomography Angle for Predicting the Outcome of Horizontal Bone Augmentation
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess the influence of ridge morphology on the amount of horizontal bone augmentation achieved with the sandwich bone augmentation (SBA) technique in the reconstruction of buccal dehiscence defects on dental implants. METHODS: Cone beam computed tomography (CBCT) was used to assess bone width changes in 26 patients who participated in a randomized controlled trial conducted in 2008 to 2011. The amount of horizontal bone gain was evaluated at four different levels (3, 6, 9, and 12 mm apical to the alveolar crest) and three different time points (T1: baseline, T2: at time of graft placement, and T3: 6 months later). Different morphological characteristics of the alveolar ridge were also evaluated to determine their influence on horizontal bone augmentation. A total of 78 CBCT scans were assessed. RESULTS: Comparison of the changes in ridge morphology at all measurement locations showed an overall ridge width gain of 2.30 ± 2.20 mm after 6 months. The use of membranes and the angulation of the concavity played a role in influencing the outcomes of the SBA technique. Critical crest angulation (CA) is 150° for bone gain at 9 mm apical to the crest. When CA is smaller than 150°, the horizontal bone gain was 4.3 ± 2.2 mm; if CA is greater than 150°, the gain was significantly lower at 1.3 ± 1.7 mm (p = .001). CONCLUSIONS: SBA is a reliable and predictable technique to gain horizontal ridge width with simultaneous implant placement. Crest ridge angulation can be used as a tool to predict bone gain at 9 mm apical to the bone crest.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".