Maxillary horizontal alveolar ridge augmentation using computer guided ridge splitting with simultaneous implant placement versus conventional technique: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Ridge splitting technique is considered one of the successful horizontal bone augmentation procedures especially for the maxillary bone, the aim of this study was to compare marginal bone loss using a novel ridge splitting protocol versus the conventional technique. MATERIAL & METHODS: This was a randomized clinical trial including 20 patients who were randomly assigned to ridge splitting with simultaneous implant placement at the anterior maxillary aesthetic zone (10 patients, 29 dental implant) using patient specific guides (PSGs) or conventional technique (10 patients, 29 dental implant). In the control group free hand ridge splitting was done, while in the study group all the splitting and drilling procedures were done through specific slits and sleeves at the patient specific guides. Radiographic Assessment included measurements of linear changes in the vertical dimensions of the labial plate of bone on cross sectional cuts of computed tomography (CBCT) using mimics software. RESULTS: Wound healing was uneventful for all the patients except one patient in the control group that showed bad split and another showed buccal fenestration. The study group showed lower bone loss (1.38 ± 0.61 mm) compared to the control group (2.42 ± 0.63 mm), with statistical significance difference (P value = 0.001). The loss percentage also was higher in the study group (10.99 ± 4.76%) compared to the control group (19.12 ± 4.53%), and there was statistical significance difference between the two groups (P value = 0.001). CONCLUSION: Ridge splitting using PSGs appear to be efficient and promising than the free hand technique.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".