On stress/strain shielding and the material stiffness paradigm for dental implants
Bibliographic record
Abstract
BACKGROUND: Stress shielding considerations suggest that the dental implant material's compliance should be matched to that of the host bone. However, this belief has not been confirmed from a general perspective, either clinically or numerically. PURPOSE: To characterize the influence of the implant stiffness on its functionality using the failure envelope concept that examines all possible combinations of mechanical load and application angle for selected stress, strain and displacement-based bone failure criteria. Those criteria represent bone yielding, remodeling, and implant primary stability, respectively MATERIALS AND METHODS: We performed numerical simulations to generate failure envelopes for all possible loading configurations of dental implants, with stiffness ranging from very low (polymer) to extremely high, through that of bone, titanium, and ceramics. RESULTS: Irrespective of the failure criterion, stiffer implants allow for improved implant functionality. The latter reduces with increasing compliance, while the trabecular bone experiences higher strains, albeit of an overall small level. Micromotions remain quite small irrespective of the implant's stiffness. CONCLUSION: The current paradigm favoring reduced implant material's stiffness out of concern for stress or strain shielding, or even excessive micromotions, is not supported by the present calculations, that point exactly to the opposite.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 | 0.000 |
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".