Comparison of marginal bone loss and implant success between axial and tilted implants in maxillary All‐on‐4 treatment concept rehabilitations after 5 years of follow‐up
Bibliographic record
Abstract
BACKGROUND: There is need for more scientific and clinical information on longer-term outcomes of tilted implants compared to implants inserted in an axial position. PURPOSE: Comparison of marginal bone loss and implant success after a 5-year follow-up between axial and tilted implants inserted for full-arch maxillary rehabilitation. MATERIAL AND METHODS: The retrospective clinical study included 891 patients with 3564 maxillary implants rehabilitated according to the All-on-4 treatment concept. The follow-up time was 5 years. Linear mixed-effect models were performed to analyze the influence of implant orientation (axial/tilted) on marginal bone loss and binary logistic regression to assess the effect of patient characteristics on occurrence of marginal bone loss >2.8 mm. Only those patients with measurements of at least one axial and one tilted implant available were analyzed. This resulted in a data set of 2379 implants (1201 axial, 1178 tilted) in 626 patients (=reduced data set). RESULTS: Axial and tilted implants showed comparable mean marginal bone losses of 1.14 ± 0.71 and 1.19 ± 0.82 mm, respectively. Mixed model analysis indicated that marginal bone loss levels at 5 years follow up was not significantly affected by the orientation (axial/tilted) of the implants in the maxillary bone. Smoking and female gender were associated with marginal bone loss >2.8 mm in a logistic regression analysis. Five-year implant success rates were 96%. The occurrence of implant failure showed to be statistically independent from orientation. CONCLUSIONS: Within the limitations of this study and considering a follow-up time of 5 years, it can be concluded that tilted implants behave similarly with regards to marginal bone loss and implant success in comparison to axial implants in full-arch rehabilitation of the maxilla. Longer-term outcomes (10 years +) are needed to verify this result.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".