<i>In vivo</i>monitoring of bone–implant bond strength by microCT and finite element modelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it