Associations of Resonance Frequency Analysis with Dynamic Mechanical Analysis of Dental Implant Systems
Bibliographic record
Abstract
BACKGROUND: Resonance frequency analysis (RFA) has been introduced as a noninvasive method to clinically estimate the stability of dental implant systems. PURPOSE: The objective of this study was to examine whether implant stability quotient (ISQ) values of RFA can account for mechanical stability of the dental implant system, which is assessed using dynamic mechanical analysis (DMA). MATERIALS AND METHODS: Fifty-seven screw-type titanium dental implants were placed in artificial polyurethane foams with seven different thicknesses (3.5 to 12 mm) and eight edentulous mandibles of human cadavers (four men and four women, 79.11 ± 13.48 years). After the ISQ values, insertion torque, and static stiffness of each implant system were measured, the DMA was performed to assess dynamic stiffness and viscoelastic tan δ. RESULTS: The ISQ value had strong positive correlations with thickness, insertion torque, static and dynamic stiffness, and a negative correlation with tan δ of implant systems in artificial bone blocks (r = 0.769 to 0.992, p < .043). However, the ISQ value was correlated with only the insertion torque of implant systems in human mandibles (p < .049). CONCLUSION: The ISQ values could reflect mechanical stability of the dental implant system under the controlled condition of homogeneous density in simple dimensions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".