Cone‐Beam Computed Tomography Evaluation of Horizontal and Vertical Dimensional Changes in Buccal Peri‐Implant Alveolar Bone and Soft Tissue: A 1‐Year Prospective Clinical Study
Bibliographic record
Abstract
PURPOSE: This study evaluated changes in the horizontal and vertical dimensions of the buccal alveolar bone and soft tissue over a 1-year period following implant prosthesis. MATERIALS AND METHODS: Thirty-three participants with no history of guided bone regeneration or soft tissue augmentation underwent dental implant placement with different types of connections. The dimensions of the buccal alveolar bone and soft tissue were evaluated immediately and at 1 year after prosthesis from reconstructions of cross-sectional cone-beam computed tomography images. RESULTS: The vertical and horizontal loss of buccal bone and soft tissue around implants with conical connections were lower than around those with external or internal connections. Statistically significant negative correlations were observed between initial horizontal bone thickness and changes in vertical bone and soft tissue height (p < .05), and between initial horizontal soft tissue thickness and the change in vertical soft tissue height (p < .05). CONCLUSION: Implants with a conical connection preserve peri-implant alveolar bone and soft tissue more effectively than other connection types. Furthermore, the initial buccal alveolar bone and soft tissue thickness around the implant platform may influence their vertical dimensional changes at 1 year after implant prosthesis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".