Implant Stability and Bone Remodeling after 3 and 13 Days of Implantation with an Initial Static Strain
Bibliographic record
Abstract
OBJECTIVE: Bone is constantly exposed to dynamic and static loads, which induce both dynamic and static bone strains. Although numerous studies exist on the effect of dynamic strain on implant stability and bone remodeling, the effect of static strain needs further investigation. Therefore, the effect of two different static bone strain levels on implant stability and bone remodeling at two different implantation times was investigated in a rabbit model. METHODS: Two different test implants with a diametrical expansion of 0.15 mm (group A) and 0.05 mm (group B) creating initial static bone strains of 0.045 and 0.015, respectively. The implants were inserted in the proximal tibial metaphysis of 24 rabbits to observe the biological response at implant removal. Both groups were compared to control implants (group C), with no diametrical increase. The insertion torque (ITQ) was measured to represent the initial stability and the removal torque (RTQ) was measured to analyze the effect that static strain had on implant stability and bone remodeling after 3 and 13 days of implantation time. RESULTS: The ITQ and the RTQ values for test implants were significantly higher for both implantation times compared to control implants. A selection of histology samples was prepared to measure bone to implant contact (BIC). There was a tendency that the BIC values for test implants were higher compared to control implants. CONCLUSION: These findings suggest that increased static bone strain creates higher implant stability at the time of insertion, and this increased stability is maintained throughout the observed period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".