Conventional Multi‐Slice Computed Tomography (CT) and Cone‐Beam CT (CBCT) for Computer‐Assisted Implant Placement. Part I: Relationship of Radiographic Gray Density and Implant Stability
Bibliographic record
Abstract
PURPOSE: The relationship of conventional multi-slice computed tomography (CT)- and cone beam CT (CBCT)-based gray density values and the primary stability parameters of implants that were placed by stereolithographic surgical guides were analyzed in this study. MATERIALS AND METHODS: Eighteen edentulous jaws were randomly scanned by a CT (CT group) or a CBCT scanner (CBCT group) and radiographic gray density was measured from the planned implants. A total of 108 implants were placed, and primary stability parameters were measured by insertion torque value (ITV) and resonance frequency analysis (RFA). Radiographic and subjective bone quality classification (BQC) was also classified. Results were analyzed by correlation tests and multiple regressions (p < .05). RESULTS: CBCT-based gray density values (765 ± 97.32 voxel value) outside the implants were significantly higher than those of CT-based values (668.4 ± 110 Hounsfield unit, p < .001). Significant relations were found among the gray density values outside the implants, ITV (adjusted r(2) = 0.6142, p = .001 and adjusted r(2) = 0.5166, p = .0021), and RFA (adjusted r(2) = 0.5642, p = .0017 and adjusted r(2) = 0.5423, p = .0031 for CT and CBCT groups, respectively). Data from radiographic and subjective BQC were also in agreement. CONCLUSIONS: Similar to the gray density values of CT, that of CBCT could also be predictive for the subjective BQC and primary implant stability. Results should be confirmed on different CBCT scanners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".