Clinical Evaluation of Split‐Crest Technique with Ultrasonic Bone Surgery for Narrow Ridge Expansion: Status of Soft and Hard Tissues and Implant Success
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the split-crest technique with ultrasonic bone surgery for implant placement in patients with narrow ridges, focusing on the status of soft and hard tissues and on implant success rate, at least 6 months after implant loading. MATERIALS AND METHODS: During September 2007 and November 2008, 15 patients received 37 implants (BTI implants) with split-crest surgical procedure using ultrasonic bone surgery. Plasma rich in growth factors (PRGF®) was applied during split crest procedure to promote tissue regeneration. Implant surfaces were humidified with PRGF to accelerate osseointegration. Patients were recalled for a final clinical evaluation at least 6 months after implant loading. Clinical assessment included the status of soft and hard tissues around implants, and implants' success rate. RESULTS: Thirty-seven implants in 15 patients were evaluated between July 2009 and January 2010. The status of soft tissues was very good, showing adequate plaque index, bleeding index, and probing depth values. Success rate of implants at the end of follow-up (between 11 and 28 months after insertion) was 100%. Bone ridge was measured and compared at final examination showing a mean ridge expansion of 3.35 mm (SD: 0.34). CONCLUSIONS: Split-crest with ultrasonic bone surgery can be considered an effective and safe procedure for narrow ridge expansion.
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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 | 0.000 |
| 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) | 0.001 | 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".