The Reliability of <scp>CBCT</scp> to Assess Quality of Augmented Bone After Lateral Sinus Floor Elevation With Xenografts: A Retrospective Analysis
Bibliographic record
Abstract
OBJETIVES: This study aimed to explore the reliability of cone beam computed tomography (CBCT) in evaluating the quality of augmented bone after lateral sinus floor elevation (LSFE) with xenografts. MATERIALS AND METHODS: Thirty-six patients with lost maxillary molars were included, with half of whom received LSFE with xenografts and staged implant placement, and the other half showed no vertical bone defects and underwent implant placement directly. A total of 36 implants were included, with 18 implants in each group. A CBCT exam was taken before implant placement to acquire data on mineral quality at the future implant site, including bone mineral density (BMD), various microstructure indices, and gray values (GVs) within different threshold ranges. Augmented bone biopsies were collected during implant preparation. The microstructure indices and histological characteristics of the biopsies were evaluated by micro computed tomography (μCT) and histological staining. An implant-oriented volume of interest for CBCT analysis was established to co-locate the CBCT-measured data and the biopsy-related data using 3DSlicer. A Spearman rank correlation test was used to analyze the relationship between CBCT-measured data and the biopsy-related data. RESULTS: μCT-measured microstructure indices of the augmented bone (BV/TV and Tb.Th) were significantly correlated with new bone area (BV/TV, p = 0.035, r = 0.498; Tb.Th, p = 0.027, r = 0.520). No correlation was found between the CBCT-measured and μCT-measured microstructure indices. CBCT-measured BMD and microstructure indices hardly showed any correlation with histological indices (p > 0.05). When the threshold was set from 0 to 50, the mean GVs were significantly, positively correlated with new bone area (p = 0.041, r = 0.486), and bone substitute area was positively correlated to the mean GVs of higher threshold (range 60-255, p = 0.048, r = 0.472; range 70-255, p = 0.009, r = 0.593). CONCLUSIONS: CBCT without bone substitute segmentation was not reliable for evaluating the quality of xenogenic augmented bone after LSFE. The influence of the xenogenic substitute on CBCT analysis can be reduced by setting a low GV threshold. The bone substitute segmentation strategy may present a new way to increase the reliability of CBCT in evaluating xenogenic augmented bone.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".