A Dynamic Analytical Model for Impact Evaluation of Percutaneous Implants
Bibliographic record
Abstract
The ongoing need for a clinically effective noninvasive technique for monitoring implant stability has led to a number of testing methods based on the concept of resonant frequency. Resonant frequency measurements are an indirect measure of the bone-implant interface integrity and do not provide any specific measures of the physical properties of the interface itself. In this study, an analytical model has been developed to interpret the measurement results of an impact testing method based on the Periotest handpiece. Model results are compared to a variety of in vitro tests to verify model predictions and to gain an understanding of the parameters influencing the measurements. Model simulations are then used to predict how changes in the supporting stiffness properties, material loss around the neck of the implant, and the presence of an implant flange will affect the measurements. The developed analytical model, in conjunction with the impact measurements, allows direct estimation of the bone properties that support implants. Model simulations show the impact testing technique to be sensitive to bone loss and stiffness changes that would correspond to poorly integrated implants (ones which may be in danger of failing). Similarly, for implants with very stiff support, little useful quantitative data can be obtained about the bone supporting the implant, as the stiffness of the other components of the system dominate the response. However, such implants are generally considered healthy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".