Comparison of Marginal Bone Loss Around Axial and Tilted Implants: A Retrospective CBCT Analysis of Up to 24 Months
Bibliographic record
Abstract
This clinical study retrospectively analyzed cone beam computed tomography (CBCT) images to determine the potential influence of implant inclination on peri-implant marginal bone loss after 18 to 24 months of functional loading. Twenty-five consecutive patients presenting with an edentulous or partially edentulous maxilla or mandible and an adequate bone volume for receiving oral implants were selected for analysis of the marginal bone loss around tilted and/or axial implants. The study population included patients rehabilitated with Toronto bridges (TBs), fixed partial dentures (FPDs), or single crowns (SCs) on axial and/or tilted implants. The primary outcome was the CBCT analysis of peri-implant marginal bone level change, depending on inclination of implants and type of prostheses. The secondary outcome was analysis of survival and success rates of tilted and axial implants. A significant difference was observed for peri-implant buccal bone loss (mean of axials 0.42 ± 0.06; mean of tilted 0.70 ± 0.09) (P = .009). The difference in peri-implant lingual/palatal/mesial/distal bone loss was not significant between axial and tilted implants (P > .05). No significant difference was observed between the marginal bone level and the type of prostheses for both tilted and axial implants in all the assessed sites (P > .05). The success rate for both tilted and axial implants was 100%, and no complications were observed for all the prosthetic rehabilitations, with a 100% survival rate. Compared to axial implants, tilted implants showed a significant statistical difference for peri-implant buccal bone loss, but no other differences were observed for peri-implant bone loss or for implant survival and success rate. Fixed partial or total rehabilitation using tilted or axial implants, or with tilted and axial implants, could be a reliable technique with advantages to patients and operators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".