Constant Strain Rate and Peri‐Implant Bone Modeling: An In Vivo Longitudinal Micro‐CT Analysis
Bibliographic record
Abstract
BACKGROUND: Strain, frequency, loading time, and strain rate, among others, determine mechanical parameters in osteogenic loading. We showed a significant osteogenic effect on bone mass (BM) by daily peri-implant loading at 1.600µε.s(-1) after 4 weeks. PURPOSE: To study the peri-implant osteogenic effect of frequency and strain in the guinea pig tibia by in vivo longitudinal micro-computed tomography (CT) analysis. MATERIAL AND METHODS: One week after implant installation in both hind limb tibiae, one implant was loaded daily for 10' during 4 weeks, while the other served as control. Frequencies (3, 10, and 30Hz) and strains varied alike in the three series to keep the strain rate constant at 1.600µε.s(-1) . In vivo micro-CT scans were taken of both tibiae: 1 week after implantation but before loading (v1) and after 2 (v2) and 4 weeks (v3) of loading as well as postmortem (pm). BM (BM (%) bone-occupied area fraction) was calculated as well as the difference between test and control sides (delta BM) RESULTS: All implants (n=78) were clinically stable at 4 weeks. Significant increase in BM was measured between v1 and v2 (p<.0001) and between v1 and v3 (p<.0001). A significant positive effect of loading on delta BM was observed in the distal peri-implant marrow 500 Region of Interest already 2 weeks after loading (p=.01) and was significantly larger (11%) in series 1 compared with series 2 (p=.006) and 3 (p=.016). CONCLUSIONS: Within the constraints of constant loading time and strain rate, the effect of early implant loading on the peri-implant bone is strongly dependent on strain and frequency. This cortical bone model has shown to be most sensitive for high force loading at low frequency.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".