Measuring the outcomes of lateral ridge augmentation using cone‐beam computed tomography
Bibliographic record
Abstract
Abstract Objectives Lateral ridge augmentation (LRA) is a surgical technique to gain bone prior to implant placement. Performing cone‐beam computed tomography (CBCT) pre‐ and post‐surgery allows for quantitative comparison of the buccal–lingual width and the vertical height of the edentulous ridges. This study used CBCT images to evaluate the bone regeneration following surgery. Methods A total of 30 cases from adult patients who underwent LRA and had high‐quality CBCT images taken pre‐ and post‐surgery from the same CBCT scanner were available for the retrospective study. Study data included linear measurements of the bone ridge width and height obtained from the middle of the edentulous ridge and a volumetric measurement of bone growth at the edentulous site observed on the CBCT scan. Results The reliability of the measurements was excellent as indicated by Intra‐Class Coefficient values of 0.974 or higher. There was a significant mean bone increase from pre‐surgery compared to post‐surgery for both the linear and volumetric measurements. The linear bone gain ranged from 1.5 to 2.5 mm and volumetric gain from 250 to 750 mm3. However, two patients did not gain any bone. Multivariate regression showed the strongest predictors of bone gain post‐surgery were the pre‐surgery bone volume and a surgical site being in the mandible. For maxillary surgical sites, particularly anterior areas, the LRA surgeries were the least successful. Conclusions LRA before implant placement helped to increase bone for the majority of patients, particularly for surgical sites in the mandible. The quantitative analyses in the CBCT images showed excellent intra‐examiner agreement.
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 | 0.001 |
| 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".