Influence of Maxillary Sinus Width on Transcrestal Sinus Augmentation Outcomes: Radiographic Evaluation Based on Cone Beam <scp>CT</scp>
Bibliographic record
Abstract
BACKGROUND: Maxillary sinus elevation is a predictable procedure to vertically enhance bone volume in the posterior maxilla for successful implant placement. It is speculated that graft bone resorption and remodeling which require angiogenesis may be affected by the dimensions of maxillary sinus cavity. PURPOSE: The aim of this study is to investigate the effect of sinus width (SW) on the outcomes of transcrestal sinus lift with simultaneous implant placement based on cone beam CT (CBCT). MATERIALS AND METHODS: A total of 57 elevated sites in 33 patients were included in this study. All the patients were treated with transcrestal sinus lift procedure associated with simultaneous implant placement using a composite graft material of autogenous bone and Bio-Oss. For each patient, CBCT scans were performed preoperatively, immediately after surgery and 6 months after surgery. Measurements of the linear parameters were conducted on the preoperative and postoperative CBCT images. The correlation of SW with graft resorption (GR) was analyzed using Pearson's correlation test with or without the classification of residual bone height. RESULTS: The average width of maxillary sinus was 13.68 ± 2.66 mm. The mean height of apical graft bone decreased from 2.85 mm immediately after surgery to 1.38 mm after 6 months. A positive association between SW and GR (r = 0.323, p = .014) was found in general. CONCLUSION: The findings show that graft bone resorption in elevated sinus has a positive correlation with the SW.
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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".