Stem cells combined with bone graft substitutes in skeletal tissue engineering
Bibliographic record
Abstract
INTRODUCTION: Bone grafting is used to repair large bone defects and autograft is recognised as producing the best clinical outcome, which is partly due to its cellular component. When autograft is unavailable, allograft and bone graft substitutes can be used; however, they rely on the host bed to provide cellular osteogenic activity. AREAS COVERED: Bone graft substitutes have the potential to benefit from the addition of stem cells aimed at enhancing the rate and quality of defect repair. Mesenchymal stem cells (MSCs) can be isolated from bone marrow or periosteum and culture expanded. Other sources of primary cells include muscle, adipose tissue, human umbilical cord and the pluripotent embryonic stem cells (ESCs). EXPERT OPINION: MSCs isolated from bone marrow have been the best characterised approach for osteogenic differentiation. Their use with synthetic scaffolds such as hydroxyapatite and tricalcium phosphate has produced promising clinical results. MSCs derived from adipose tissue, muscle or human umbilical cord cells combined with various scaffolds are an attractive option. Further in vivo and clinical investigation of their potential is required. Pluripotent ESCs have a theoretical advantage over MSCs; however, purification, cell-specific differentiation, effective delivery vehicles-scaffolds and teratogenesis control are still under in vitro and in vivo evaluation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 0.004 |
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".