Characterization of a Calcium Phosphate-Based Matrix for rhBMP-2
Bibliographic record
Abstract
Bone morphogenetic proteins (BMPs) are members of the transforming growth factor-β (TGF-β) superfamily of proteins that have crucial roles in growth and regeneration of skeletal tissues. In particular, recombinant human bone morphogenetic protein-2 (rhBMP-2) has been shown to induce new bone formation by differentiation of mesenchymal progenitor cells into osteoblasts ( 1 ). BMPs act locally, and thus for a clinically beneficial outcome, a carrier matrix is required to retain therapeutic levels of BMP at the repair site ( 2 , 3 ). The potent osteoinductive ability of BMP-2 has been demonstrated in numerous preclinical ( 4 – 9 ) and clinical studies ( 10 – 14 ). Recent data from clinical trials have reported success in the areas of open fracture repair ( 10 , 14 ), interbody spinal fusion ( 11 ), and maxillofacial reconstruction ( 12 , 13 ) using rhBMP-2 delivered in an absorbable collagen sponge (ACS). Because rhBMP-2/ACS requires implantation, there is a continuing need for an easily injectable carrier to deliver rhBMP-2 percutaneously to closed fractures. Here, we outline characterization of a calcium phosphate (CaP)-based injectable carrier as a delivery matrix for rhBMP-2. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".