Spatial Subchondral Bone Density Reflecting Joint Loading of the Talus in Different Canidae
Bibliographic record
Abstract
Objective Subchondral bone density distribution can be used to study joint biomechanics non-invasively. Differences in joint loading between related species can aid in the understanding of joint loading and the development of certain types of orthopaedic pathology. This study was conducted to evaluate density distribution in the subchondral bone of the talus of different Canidae species, as a parameter reflecting the long-term joint loading in the tarsocrural joint. Materials and Methods The tarsal joints of cadaveric dogs of different breeds were included, that is, German Shepherd (n = 5), Bouvier des Flandres (n = 3) and Labrador Retriever (n = 6). Additionally, golden jackals (n = 5) (Canis aureus) and wolves (n = 5) (Canis lupus) were included. Consecutive computed tomography slices were made and the subchondral bone density distribution was evaluated using computer tomographic osteoabsorptiometry. Different breeds and species were visually compared. Results Differences were found in the subchondral bone density distribution of the talus between breeds and between species (Canis familiaris, Canis lupus and Canis aureus). Discussion and Conclusion Based on the density distribution, there are differences in loading conditions of the tarsocrural joint in different species of Canidae. The joint loading distribution is very similar between dogs of the same breed and within the same species. Although between-breed differences can be explained by conformational differences, the between-species differences remain subject to further research.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".