Remodeling- and Modeling-Based Bone Formation With Teriparatide Versus Denosumab: A Longitudinal Analysis From Baseline to 3 Months in the AVA Study
Bibliographic record
Abstract
There has been renewed interest of late in the role of modeling-based formation (MBF) during osteoporosis therapy. Here we describe early effects of an established anabolic (teriparatide) versus antiresorptive (denosumab) agent on remodeling-based formation (RBF), MBF, and overflow MBF (oMBF) in human transiliac bone biopsies. Postmenopausal women with osteoporosis received subcutaneous teriparatide (n = 33, 20 μg/d) or denosumab (n = 36, 60 mg once/6 months), open-label for 6 months at 7 US and Canadian sites. Subjects received double fluorochrome labeling at baseline and before biopsy at 3 months. Sites of bone formation were designated as MBF if the underlying cement line was smooth, RBF if scalloped, and oMBF if formed over smooth cement lines adjacent to scalloped reversal lines. At baseline, mean RBF/bone surface (BS), MBF/BS, and oMBF/BS were similar between the teriparatide and denosumab groups in each bone envelope assessed (cancellous, endocortical, periosteal). All types of formation significantly increased from baseline in the cancellous and endocortical envelopes (differences p < 0.001) with teriparatide (range of changes 2.9- to 21.9-fold), as did MBF in the periosteum (p < 0.001). In contrast, all types of formation were decreased or not significantly changed with denosumab, except MBF/BS in the cancellous envelope, which increased 2.5-fold (difference p = 0.048). These data highlight mechanistic differences between these agents: all 3 types of bone formation increased significantly with teriparatide, whereas formation was predominantly decreased or not significantly changed with denosumab, except for a slight increase in MBF/BS in the cancellous envelope. © 2017 American Society for Bone and Mineral Research.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".