Altered topological blueprint of trabecular bone associates with skeletal pathology in humans
Bibliographic record
Abstract
Bone is a hierarchically organized biological material, and its strength is usually attributed to overt factors such as mass, density, and composition. Here we investigate a covert factor - the topological blueprint, or the network organization pattern of trabecular bone. This generally conserved metric of an edge-and-node simplified presentation of trabecular bone relates to the average coordination/valence of nodes and the equiangular 3D offset of trabeculae emanating from these nodes. We compare the topological blueprint of trabecular bone in presumably normal, fractured osteoporotic, and osteoarthritic samples (all from human femoral head, cross-sectional study). We show that bone topology is altered similarly in both fragility fracture and in joint degeneration. Decoupled from the morphological descriptors, the topological blueprint subjected to simulated loading associates with an abnormal distribution of strain, local stress concentrations and lower resistance to the standardized load in pathological samples, in comparison with normal samples. These topological effects show no correlation with classic morphological descriptors of trabecular bone. The negative effect of the altered topological blueprint may, or may not, be partly compensated for by the morphological parameters. Thus, naturally occurring optimization of trabecular topology, or a lack thereof in skeletal disease, might be an additional, previously unaccounted for, contributor to the biomechanical performance of bone, and might be considered as a factor in the life-long pathophysiological trajectory of common bone ailments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".