Quantitative Comparison of Cone Beam Computed Tomography and Microradiography in the Evaluation of Bone Density after Maxillary Sinus Augmentation: A Preliminary Study
Bibliographic record
Abstract
PURPOSE: Cone beam computed tomography (CBCT) and microradiographic analyses were comparatively performed in maxillary sinus augmentation to preliminarily verify the diagnostic potential of CBCT on the evaluation of bone regeneration. MATERIALS AND METHODS: A two-stage protocol was conducted in 19 consenting patients, all having the crestal bone ≤2 mm, in private dental office. Mineralized human bone allograft particles were used to augment sinus using lateral window approach. A succession of CBCT scans of the maxilla was taken before surgery, after sinus augmentation, and immediately after implant insertion. Using virtual probes, CBCT data were processed by medical imaging software and expressed as gray level (GL). A bone core biopsy was taken at implant placement, 6 months after surgery. Microradiography of transverse sections, taken 6, 8, and 10 mm from the crestal surface, of methacrylate-embedded biopsies was performed to analyze and to evaluate the mineralized material amount (MM%). RESULTS: A total of 21 sinus augmentations were performed. CBCT (mean GL: 646-693) data were not statistically different when comparing 6-, 8-, and 10-mm sites to after grafting/implant-insertion values. Furthermore, microradiographic (mean MM%: 45.3-48.3) data were not statistically different comparing 6-, 8-, and 10-mm sites, due to variation of values among patients. A GL and MM% parallelism was identified considering each patient, instead. A significant correlation (p < .001) between GL and MM% was found after both Wilcoxon test for paired data and simple linear regression analysis. CONCLUSIONS: The preliminary result clearly demonstrated the predictability of the CBCT analysis. Due to the limited sample and great variations of the MM% recorded in patients, further clinical and morphometric studies are needed to fulfill diagnostic expectations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".