Mechanical regulation of localized and appositional bone formation around bone-interfacing implants
Bibliographic record
Abstract
The local mechanical environment around bone-interfacing implants determines, in large part, whether bone formation leading to functional osseointegration will occur. Previous attempts to relate local peri-implant tissue strains to tissue formation have not accounted for implant surface geometry, which has been shown to influence early tissue healing in vivo. Furthermore, the process by which mechanically regulated peri-implant bone formation occurs has not been considered previously. In the current study, we used a unit cell approach and the finite element method to predict the local tissue strains around porous-surfaced and plasma-sprayed implants, and compared the predictions to patterns of bone formation reported in earlier in vivo experiments. Based on the finite element predictions, we determined that appositional bone formation occurred when the magnitudes of the strain components at the tissue-host bone interface were <8%. Localized, de novo bone formation occurred when the distortional tissue strains were less than approximately 3%. Based on these threshold tissue strains, we propose a mechanoregulatory model to relate local tissue strains to the process of peri-implant bone formation. The mechanoregulatory model is novel in that it predicts both appositional and localized bone formation and its predictions are dependent on implant surface geometry. The model provides initial criteria with which the osseointegration potential of bone-interfacing implants may be evaluated, particularly under conditions of immediate or early loading.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".