Osteoprogenitor Cell Therapy in an Equine Fracture Model
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of osteoprogenitors in fibrin glue to fibrin glue alone in bone healing of surgically induced ostectomies of the fourth metacarpal bones in an equine model. STUDY DESIGN: Experimental. ANIMALS: Adult horses (n = 10). METHODS: Segmental ostectomies of the 4th metacarpal bone (MC4) were performed bilaterally in 10 horses. There was 1 treatment and 1 control limb in each horse. Bone defects were randomly injected with either fibrin glue and osteoprogenitor cells or fibrin glue alone. Radiography was performed every week until the study endpoint at 12 weeks. After euthanasia, bone healing was evaluated using radiography and histology. Analysis of radiographic data was conducted using a linear-mixed model. Analysis of histologic data was conducted using a general linear model. Statistical significance was set at P < .05. RESULTS: Radiographic grayscale data as a measure of bone healing revealed no significant difference between treatment and control limbs. Radiographic scoring results also showed that the treatment effect was not significant. Histologic analysis was consistent with radiographic analysis showing no significant difference between the area of bone present in treatment and control limbs. CONCLUSION: Injection of periosteal-derived osteoprogenitors in a fibrin glue carrier into surgically created ostectomies of MC4 does not accelerate bone healing when compared with fibrin glue alone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".