An improved impact technique for monitoring percutaneous implant integrity.
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the validity of the current Periotest system when measuring implant systems and to present a new system to monitor implant interface integrity. MATERIALS AND METHODS: The new system records an impact accelerometer signal and utilizes software for data analysis to determine the resonance frequency of an implant-abutment system. The new system uses the handpiece from the Periotest to acquire an impact signal but makes no use of the rest of the device. Tests were completed to determine the repeatability of the new system along with the effects clinical variables such as abutment torque, angulation of the handpiece, striking height, and distance handpiece is held from the abutment have on the measurement results. Accuracy of the current Periotest method as well as the new system was independently evaluated through the use of an abutment with a strain gauge attached. RESULTS: The new system for impact testing is shown to have greater accuracy than that of the Periotest device. Additionally, the effects of handpiece distance from abutment and torque (when above 15 Ncm) were found to be negligible while angulation of the handpiece and striking height affected the resonance frequency of the new system. CONCLUSION: The results of the in vitro testing indicate that greater resolution and accuracy can be achieved from an impact test that utilizes a clinical measurement protocol and independent analysis of the impact accelerometer signal.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".