Modeling the effect of osseointegration on dental implant pullout and torque removal tests
Bibliographic record
Abstract
BACKGROUND: Osseointegration of dental implants is a key factor for their success. It can be assessed either by destructive (eg, pullout or torque extraction), or nondestructive methods (eg, resonant frequency analysis). However, as of today there is a scarcity of models that can relate the outcome of destructive tests to the level of osseointegration. PURPOSE: To study various percentages of bone to implant bonding (tie) using finite element simulations. While evolutions of the bone mechanical properties are not explicitly taken into account, emphasis is put on the 3-dimensional variable extent of the bone-implant bonding, its statistical distribution, and its influence on the measurable extraction and torque loads, seeking to obtain a quantitative relationship. MATERIALS AND METHODS: We performed numerical simulations of randomly tied implants and calculated the evolution of the pullout force as well as that of the extraction torque. CONCLUSION: Within simplifying assumptions for the osseointegration represented by a tie (as opposed to frictional) constraint, the results of this work indicate that the torque test is more discriminant than the extraction one, while both cannot really discriminate osseointegration levels below a relative variation of 20%.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".