Evaluation of dental implant stability in bone phantoms: Comparison between a quantitative ultrasound technique and resonance frequency analysis
Bibliographic record
Abstract
BACKGROUND: Resonance frequency analyses and quantitative ultrasound methods have been suggested to assess dental implant primary stability. PURPOSE: The purpose of this study was to compare the results obtained using these two techniques applied to the same dental implants inserted in various bone phantoms. MATERIALS AND METHODS: Different values of trabecular bone density and cortical thickness were considered to assess the effect of bone quality on the respective indicators (UI and ISQ). The effect of the implant insertion depth and of the final drill diameter was also investigated. RESULTS: ISQ values increase and UI values decrease as a function of trabecular density, cortical thickness and the screwing of the implant. When the implant diameter varies, the UI values are significantly different for all final drill diameters (except for two), while the ISQ values are similar for all final drill diameters lower than 3.2 mm and higher than 3.3 mm. The error on the estimation of parameters with the QUS device is between 4 and 8 times lower compared to that made with the RFA technique. CONCLUSIONS: The results show that ultrasound technique provides a better estimation of different parameters related to the implant stability compared to the RFA technique.
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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 | 0.005 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".