One‐Year Outcome of Implants Strategically Placed in the Retrocanine Bone Triangle
Bibliographic record
Abstract
BACKGROUND: Implant treatment in the partially edentulous maxilla is often challenging because of minimum bone volumes in distal direction. PURPOSE: The aim of this study was to evaluate, after 1 year of loading, the outcome of three-unit fixed partial dentures supported by two implants in the retrocanine triangle. MATERIALS AND METHODS: Twenty patients with atrophic posterior maxillae participated in the study. A total of 40 implants were placed in residual bone anterior to the sinus wall and posterior to the canine. Implant angulations and lengths were chosen to match as much as possible boundaries of the available bone. After a 6-month healing period, three-unit, screw-retained, fixed partial dentures were delivered. The patients were clinically and radiographically reexamined after 1 year of loading. RESULTS: All the implants survived at the end of the follow-up. No differences in bone level changes resulted between axial and tilted implants. No biological or mechanical complications were recorded. CONCLUSIONS: Within the limitations of this short-term study on relatively few patients, a positive outcome was seen for three-unit fixed partial dentures supported by two implants. Retrocanine placement of implants with carefully planned lengths and angulations might be an alternative to grafting procedures for restoration of atrophic posterior maxillae.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".