Evaluation of a vibration modeling technique for the in-vitro measurement of dental implant stability
Bibliographic record
Abstract
The Advanced System for Implant Stability Testing (ASIST) is a device currently being developed to noninvasively measure implant stability by estimating the mechanical stiffness of the bone-implant interface, which is reported as the ASIST Stability Coefficient (ASC). This study's purpose was to determine whether changes in density, bonding, and drilling technique affect the measured vibration of a dental implant, and whether they can be quantified as a change in the estimated BII stiffness. Stability was also measured using RFA, insertion torque (IT) and the pullout test. Bone-level tapered implants (4.1 mm diameter, 10 mm length) were inserted in polyurethane foam as an artificial bone substitute. Samples were prepared using different bone densities (20, 30, 40 PCF), drilling sequences, and superglue to simulate a bonded implant. Measurements were compared across groups at a significance level of 0.05. The ASC was able to indicate changes in each factor as a change in the interfacial stiffness. IT and pullout force values also showed comparable increases. Furthermore, the relative difference in ISQ values between experimental groups was considerably smaller than the ASC. While future work should be done using biological bone and in-vivo systems, the results of this in-vitro study suggest that modelling of the implant system with a vibration-based approach may provide a noninvasive method of assessing the mechanical stability of the implant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".