Relationship of implant stability and bone density derived from computerized tomography images
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Implant stability is one of the most important factors influencing osseointegration. Using stereolithographical guides for maximizing precision, this study aimed at investigating the relationship between implant stability and bone density derived from computerized tomography analysis. MATERIALS AND METHODS: One hundred ninety-five implants were placed in 48 patients using digitally designed stereolithographical surgical guides. Ninety-five implants were placed using a mucosa supported guide and 100 implants were placed using a bone supported guide. Implant stability was measured by means of resonance frequency analysis (RFA) and damping capacity assessment (Periotest, PTV). Bone density (Hounsfield units) was measured at different regions of interest (ROI) and cortex thickness was measured around each implant. RESULTS: Implant stability correlated significantly with the different ROI. The best correlation for RFA was obtained for the spongious bone ROI (r = .64) and PTV best correlated with the coronal cortex density (r = -.41). Shorter implants (9 mm) had a significantly lower primary stability than longer implants (11, 13, 15 mm). Primary stability was also significantly higher in 4 mm diameter implants than in 3.5 mm diameter implants. A formula for the prediction of primary stability based on the different variables investigated was developed. CONCLUSIONS: Bone density and cortex thickness have a significant influence on implant primary stability. Longer and wider implants reached higher primary stability than shorter and narrower implants. These correlations lose their significance after osseointegration has taken place. Implant stability can be predicted based on an preoperative analysis of bone characteristics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".