Horizontal ridge augmentation in the maxillary aesthetic region using the autogenous circular cortical‐lamina anchoring technique: A case series study
Bibliographic record
Abstract
Abstract Aim This case series aimed to evaluate the effectiveness of the autologous circular cortical lamina‐anchoring (CCA) technique for horizontal bone augmentation in the maxillary aesthetic region. Materials and methods A total of 25 patients with 28 implants underwent horizontal bone augmentation using CCA followed by implant placement and crown delivery. The primary outcome measures were alveolar ridge width (ARW) and buccal bone thickness (BBT), whereas the secondary outcome measures included marginal bone loss (MBL), mid‐facial mucosal margin loss (MML), clinical assessment of peri‐implant and aesthetic parameters, patient‐reported outcome measures (PROMs), and implant survival rates. Results All 25 patients with 28 implants completed the treatment, no dropouts occurred. After CCA, the mean ARW at 1, 2, and 4 mm below the alveolar crest significantly increased from 2.38 ± 0.48, 2.85 ± 0.51, and 3.21 ± 0.53 mm to 6.80 ± 0.48, 6.99 ± 0.50, and 8.08 ± 0.52 mm, respectively. At the 3‐year follow‐up, the mean BBT0, BBT2, and BBT4 slightly decreased from 2.51 ± 0.26, 2.63 ± 0.31, and 2.75 ± 0.29 mm to 2.43 ± 0.27, 2.51 ± 0.30, and 2.64 ± 0.28 mm, respectively. Although the overall MBL was <0.15 mm, the results were statistically significant. The mean MML at the 3‐year follow‐up was 0.02 mm. All implant sites showed acceptable peri‐implant and aesthetic outcomes. Incisions healed without complications, and no significant differences in PROMs observed at any time point. The 3‐year follow‐up showed a 100% implant survival rate. Conclusion The autologous CCA technique is a useful method for increasing ARW and maintaining BBT in the maxillary aesthetic region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".