Ex Vivo and In Vivo Biomechanical Test of Implant Attachment to Various Materials: Introduction of a New User‐Friendly Removal Torque Equipment
Bibliographic record
Abstract
OBJECTIVE: The removal torque (RTQ) analysis is commonly used for biomechanical evaluation of osseointegration. The overall aim of this study was to verify results obtained with a newly developed equipment for biomechanical testing of osseointegration. METHODS: Verification of the new equipment for biomechanical tests involved three experiments: Part I, comparison of RTQ between implants placed in four different types of dental synthetic plasters. Part II, comparison of RTQ between custom made, experimentally used implants to self-tapping, commercially available implants molded in the same type of dental plaster. Part III, comparison of RTQ between commercially pure titanium implants to Ti6Al4V implants placed in rabbit bone, 6 weeks after insertion. Briefly, for all experiments, the peak RTQ values and the removal process were recorded every 0.01 seconds up to 10 seconds. After the measurements, peak RTQ values were converted to shear strength. RESULTS: The developed equipment sensitively responded to the changes of properties related to the molding plasters, implant topographies, and materials. The monitored graphs corresponded well to the expected properties of the different implants and tested materials. CONCLUSION: The new RTQ equipment proved to be accurate and could add new knowledge in understanding the biomechanical aspects of osseointegration.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".