<i>In vivo</i>monitoring of bone–implant bond strength by microCT and finite element modelling
Bibliographic record
Abstract
Immediately after implantation, a dynamic process of bone formation and resorption takes place around an orthopaedic implant, influencing its mechanical fixation. The delay until complete fixation depends on local bone architecture and metabolism. Despite its importance, the temporal pattern of implant fixation is still unknown. The optimal duration of post-operative care is therefore difficult to establish for an individual situation, and a method to evaluate non-invasively the evolution of the mechanical stability would be a significant asset in a clinical environment. The aim of this study was to evaluate the potential of micro-finite element modelling based on in vivo micro-computed tomography to monitor longitudinally the contact between bone and implant and the implant strength in vivo. The model was first validated for screw pull-out in synthetic bone surrogate. Correlation coefficients of R(2) = 0.94 and 0.85 (p < 0.01) were measured between experimental and numerical results for stiffness and failure loads, respectively. Then, the mechanical integration of screws in the proximal tibia of 12 rats was monitored at seven time points over a period of 1 month. We observed significant increases (p < 0.05) of bone-screw contact (+28%), stiffness (+93%) and failure load (+71%) over the course of the experiment, and more than 75% of these changes occurred during the first 2 weeks. Limitations, such as image artefacts and radiation, still compromise the immediate clinical application of this method, but it has a promising potential in preclinical animal studies, as it provides very valuable data about the dynamic aspect of implant integration with considerably reduced animal resources.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".