Evaluation of a Novel Cone Beam Computed Tomography Scanner for Bone Density Examinations in Preoperative 3<scp>D</scp> Reconstructions and Correlation with Primary Implant Stability
Bibliographic record
Abstract
BACKGROUND: Bone density examination (BDE) using preoperative cone beam computed tomography (CBCT) might be used to predict primary implant stability in implant patients. PURPOSE: The aim of the study was to validate a novel CBCT scanner in vitro with regard to BDE in preoperative scans and to analyze the in vivo correlation of CBCT scan results with primary implant stability measurements. MATERIALS AND METHODS: A CBCT scanner was validated in vitro with regard to spatial uniformity and linearity of CT numbers (Hounsfield units, HU) by using a series of phantoms and plastic and hydroxyapatite specimens of various densities. Forty-nine patients (27 female and 22 male, mean age 55.6 ± 9.8) were scanned prior to and 1 to 6 months after the placement of 155 dental implants of different lengths and diameters. Mean and peak insertion torque (IT) in Ncm were registered during implant placement. Resonance frequency analysis (RFA) measurements in ISQ units were performed after placement. The second scan was used to export and superimpose the exact positions of bone and implants into the first scan. Virtual probes with the same length as the actual implant were automatically placed at the implant sites, and mean HU values were measured in a 1 mm-wide circular corridor from the tip of the threads and out. RESULTS: The in vitro validation showed high uniformity and linearity of CT numbers (HU). The clinical study showed significant correlations between bone density and ISQ, mean IT, and peak IT, respectively. CONCLUSIONS: The WhiteFox CBCT scanner measures bone density with high accuracy. There is a correlation between bone density and primary implant stability as assessed with IT and RFA measurements. The findings suggest that BDE may be used as an additional feature in treatment-planning software to estimate primary stability at predetermined implant sites.
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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 | 0.005 |
| 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 | 0.001 | 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".