Narrow‐Diameter versus Standard‐Diameter Implants and Their Effect on the Need for Guided Bone Regeneration: A Virtual Three‐Dimensional Study
Bibliographic record
Abstract
PURPOSE: Narrow-diameter implants (NDIs) are proven treatment options for completely edentulous patients with severely resorbed alveolar ridges. The aim of this study was to evaluate virtually whether or not the implant diameter affects the need for ridge augmentation in edentulous patients, using a 3D planning software program. MATERIALS AND METHODS: Existing cone beam CT scans of 200 outpatients (100 maxillae, 100 mandibles) were selected, and treatment was planned in a virtual 3D planning software program with either 3.3 mm-diameter implants (test group) or 4.1 mm-diameter implants (control group). Statistical analysis was performed. RESULTS: A total of 1,760 implants were virtually planned (880 implants each for test and control groups). Overall, significantly associated with the absence or need for ridge augmentation as compared with need for ridge augmentation (p < .0001). Use of the 3.3 mm-diameter implants increased the odds ratio for ridge augmentation being unnecessary by 2.2 (95% confidence interval) relative to the 4.1 mm-diameter implants. CONCLUSIONS: Use of NDIs was able to provide a statistically significant reduction in need for bone grafting among completely edentulous patients. More clinical longitudinal studies are necessary to confirm the long-term success of their use.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".