The Use of Tilted Implant for Posterior Atrophic Maxilla
Bibliographic record
Abstract
PURPOSE: To retrospectively analyze the influence of implant inclination on marginal bone loss at freestanding implant-supported fixed partial prostheses (FPPs) over a medium-term period of functional loading. MATERIALS AND METHODS: Twenty-nine partially edentulous patients with freestanding FPDs supported by two implants placed in a two-stage procedure comprised the study group. The anterior implant was placed axially, and the posterior tilted distally. Mesial or distal inclination of each implant was measured in relation to the vertical axis perpendicular to the occlusal plane. Average bone loss was compared between straight and tilted implants, smokers, and nonsmokers. RESULTS: Mean angulation of the anterior axial-positioned implant was 3.45 degrees distally (range 0-8) and of the distal implants was 32.83 degrees distally (range 20-50 degrees). Average bone loss after 1, 3, and 5 years was 0.89 (SD = 0.73), 1.18 (SD = 0.74), and 1.50 (SD = 0.81), respectively, for axial implants, and 0.98 (SD = 0.69), 1.10 (SD = 0.60) and 1.50 (SD = 0.67) for tilted implants, with no significant correlation between implant angulation and bone loss. A significant correlation between implant angulation and annual bone loss was obtained for tilted implants only (r = 0.52, p = .004).Using Albrektsson criteria, the success rate was 89.6% (26 out of 29 implants) for straight and 93.1% (27 out of 29) for tilted implants. CONCLUSION: The study demonstrates no effect of implant angulation on peri-implant bone loss in the posterior maxilla.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".