Cortical Bone Porosity in Rabbit Models of Osteoporosis
Bibliographic record
Abstract
Cortical bone porosity is intimately linked with remodeling, is of growing clinical interest, and is increasingly accessible by imaging. Thus, the potential of animal models of osteoporosis (OP) to provide a platform for studying how porosity develops and responds to interventions is tremendous. To date, rabbit models of OP have largely focused on trabecular microarchitecture or bone density; some such as ovariectomy (OVX) have uncertain efficacy and cortical porosity has not been extensively reported. Our primary objective was to characterize tibial cortical porosity in rabbit-based models of OP, including OVX, glucocorticoids (GC), and OVX + GC relative to controls (SHAM). We sought to: (i) test the hypothesis that intracortical remodeling is elevated in these models; (ii) contrast cortical remodeling and porosity in these models with that induced by parathyroid hormone (1-34; PTH); and (iii) contrast trabecular morphology in the proximal tibia across all groups. Evidence that an increase in cortical porosity occurred in all groups was observed, although this was the least robust for GC. Histomorphometric measures supported the hypothesis that remodeling rate was elevated in all groups and also revealed evidence of uncoupling of bone resorption and formation in the GC and OVX + GC groups. For trabecular bone, a pattern of loss was observed for OVX, GC, and OVX + GC groups, whereas the opposite was observed for PTH. Change in trabecular number best explained these patterns. Taken together, the findings indicated rabbit models provide a viable and varied platform for the study of OP and associated changes in cortical remodeling and porosity. Intriguingly, the evidence revealed differing effects on the cortical and trabecular envelopes for the PTH model. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)..
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".