Cortical bone loss in osteoporosis: the rabbit as a platform for drug discovery and testing
Bibliographic record
Abstract
INTRODUCTION: Osteoporosis (OP) affects bone quality and quantity of millions of people worldwide. Osteoporotic fractures significantly decrease the quality of life of patients and are associated with increased mortality in the following years. Thus, there is continued clinical interest in treatments that preserve bone and mitigate fracture risk. As a routine method, several preclinical animal models exist to test current and potential OP treatments. However, most studies focus on trabecular bone, while cortical bone is under-studied, despite its significant role in bone strength and fragility. AREAS COVERED: The authors review the available on the use of the rabbit model to investigate the pathophysiology and treatments (antiresorptive and osteoanabolic) of OP, emphasizing cortical bone outcomes. Google Scholar was utilized to find the most up-to-date literature on the subject. EXPERT OPINION: The rabbit model of OP is suitable choice for investigation treatments in cortical bone, owing to its human-like cortical remodeling process, which is fundamental to the development of OP. Opportunities exist to utilize novel imaging and histological methods with the rabbit model to examine the mechanisms underpinning the pathophysiology of OP, and to investigate existing and new targets for drug discovery at a microscopic level within the cortical bone compartment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".