Fabric-elasticity relationships of tibial trabecular bone are similar in osteogenesis imperfecta and healthy individuals
Bibliographic record
Abstract
Osteogenesis Imperfecta (OI) is an inherited form of bone fragility characterised by impaired synthesis of type I collagen, altered trabecular bone architecture and reduced bone mass. High resolution peripheral computed tomography (HR-pQCT) is a powerful method to investigate bone morphology at peripheral sites including the weight-bearing distal tibia. The resulting 3D reconstructions can be used as a basis of micro-finite element (FE) or homogenized finite element (hFE) models for bone strength estimation. The hFE scheme uses homogenized local bone volume fraction (BV/TV) and anisotropy information (fabric) to compute healthy bone strength within a reasonable computation time using fabric-elasticity relationships. However, it is unclear if these relationships quantified previously for healthy controls are valid for trabecular bone from OI patients. Thus, the aim of this study is to investigate fabric-elasticity relationships in OI trabecular bone compared to healthy controls. In the present study, the morphology of distal tibiae from 50 adults with OI were compared to 120 healthy controls using second generation HR-pQCT. Six cubic regions of interest (ROIs) were selected per individual in a common anatomical region. A first matching between OI and healthy control group was performed by selecting similar individuals to obtain identical mean and median age and sex distribution. It allowed us to perform a first morphometric analysis and compare the outcome with literature. Then, stiffness tensors of the ROIs were computed using μFE and multiple linear regressions were performed with the Zysset-Curnier orthotropic fabric-elasticity model. An initial fit was performed on both the OI group and the healthy control group using all extracted ROIs. Then, data was filtered according to a fixed threshold for a defined coefficient of variation (CV) assessing ROI heterogeneity and additional linear regressions were performed on these filtered data sets. These full and filtered data were in turn compared with previous results from μCT reconstructions obtained in other anatomical locations. Finally, the ROIs of both groups were matched according to their BV/TV and degree of anisotropy (DA). Linear regressions were performed using these matched data to detect statistical differences between the two groups. Compared to healthy controls, we found the OI samples to have significantly lower BV/TV and trabecular number (Tb.N.), significantly higher CV, trabecular separation (Tb.Sp.) and trabecular separation standard deviation (Tb.Sp.SD), but no differences in trabecular thickness (Tb.Th.). These results are in agreement with previous studies. The stiffnesses of highly heterogeneous ROIs were randomly lower with respect to the fabric-elasticity relationships, which reflects the limit of validity of the computational homogenisation methodology. This limitation does not challenge the fabric-elasticity relationship, which extrapolation to heterogeneous ROIs is probably reasonable but can simply not be evaluated with the employed homogenisation methodology. Moreover, due to their low BV/TV, the potential (unknown) errors on these heterogeneous ROIs would have negligible influence on whole bone stiffness in comparison to homogeneous ROIs which are orders of magnitude stiffer. The filtering of highly heterogeneous ROIs removed these low stiffness ROIs and led to similar correlation coefficients for both OI and healthy groups. Finally, the BV/TV and DA matched data revealed no significant differences in fabric-elasticity parameters between OI and healthy individuals. Moreover, the filtering step did not exclude a particular OI type. Compared to previous studies, the stiffness constants from the 61 μm resolution HR-pQCT ROIs were lower than for the 36 μm resolution μCT ROIs. In conclusion, OI trabecular bone of the distal tibia was shown to be significantly more heterogeneous and have a lower BV/TV than healthy controls. Despite the reduced linear regression parameters found for HR-pQCT images, the fabric-elasticity relationships between OI and healthy individuals are similar when the trabecular bone ROIs are sufficiently homogeneous to perform the computational stiffness analysis. Accordingly, the elastic properties used for FEA of healthy bones are also valid for OI bones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".