Implant rehabilitation of the edentulous jaws: Does tilting of posterior implants at an angle greater than 45° affect bone resorption and implant success?: A retrospective study
Bibliographic record
Abstract
PURPOSE: This study aimed to (1) investigate the success of posterior implants tilted >45° when 4 immediately loaded implants were used to support full-arch prostheses, eliminating any distal cantilever and (2) examine the effect on marginal bone loss (MBL) of different combinations of anterior multi-unit abutment (MUA) angles and posterior implant tilting angles. MATERIALS AND METHODS: Records of patients rehabilitated according to the Columbus Bridge Protocol were analyzed. Peri-implant bone levels (PBLs) and MBL were measured for each implant. The influence of posterior implant tilting angle on PBL, MBL, and implant and prosthetic success rate was investigated. The impact on the same endpoints of different anterior MUA angles, and different combinations of anterior MUA and tilted posterior implant angles was also examined. RESULTS: Records of 41 patients were analyzed, for a total of 46 complete rehabilitations, and 142 implants (52 anterior, 63 posterior tilted ≤45° [group 1], and 27 posterior tilted >45° [group 2]). No implants were lost during the follow-up (25.9 months), and no prosthetic complications were reported. Success rate for posterior implants was 100% in group 1 and 96.3% in group 2. Mean MBL differed significantly between the 2 groups (0.45 mm in group 1, 0.66 in group 2 [P = .04]), but not when the analysis was limited to implants in the same jaw. Implant tilting angle did not correlate with MBL and the MUA angle had no effect on bone resorption around posterior implants, neither in the sample as a whole nor in individual patients. CONCLUSIONS: Posterior implants tilted >45° to eliminate distal cantilever may be as safe as those tilted less in severely atrophic jaws rehabilitated with immediately loaded, full-arch prostheses supported on 4 implants. Further prospective studies on larger samples of patients and implants and with longer follow-up are needed to confirm these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".