Intra‐articular injection of rhFGF‐18 improves the healing in microfracture treated chondral defects in an ovine model
Bibliographic record
Abstract
Microfracture is a common cartilage repair procedure. Strategies to improve healing post-microfracture include the use of growth factors to enhance hyaline cartilage production. This study investigates the effect of intra-articular recombinant human fibroblastic growth factor 18 (rhFHF18) on the healing of a chondral defect treated with microfracture in an ovine model. Chondral defects (8 mm diameter) were created in the medial femoral condyle of 80 sheep (n = 16/treatment group). Defects were treated with microfracture alone or microfracture + intra-articular rhFGF-18 (administered either as one or two cycles of 3× weekly injections). Outcome measures included mechanical testing, macroscopic International Cartilage Repair Society repair score, modified O'Driscoll histology score, qualitative histology, and immunohistochemistry for types I, II, and VI collagen. In treated animals, there was a statistically significant improvement in ICRS tissue repair score and tissue infill score, in the modified O'Driscoll score between control and 1 cycle of rhFGF-18 at 6 m, and in the cartilage repair score and structural characteristic score between the control and both rhFGF-18 groups at 6 m. There was no evidence of degeneration of adjacent cartilage in the rhFGF-18 treated cartilage. The increase in hyaline cartilage-like tissue formed in the microfracture + rhFGF-18 treated groups indicates that rhFGF-18 potentiates the formation of hyaline cartilage repair following microfracture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".