The Developing Field of Scaffold-Free Tissue Engineering for Articular Cartilage Repair
Bibliographic record
Abstract
Articular cartilage is critical for proper joint mobility as it provides a smooth and lubricated surface between articulating bones and allows for transmission of load to underlying bones. Extended wear or injury of this tissue can result in osteoarthritis, a degenerative disease affecting millions across the globe. Because of its low regenerative capacity, articular cartilage cannot heal on its own and effective treatments for injured joint restoration remain a challenge. Strategies in tissue engineering have been demonstrated as potential therapeutic approaches to regenerate and repair damaged articular cartilage. Although many of these strategies rely on the use of an exogenous three-dimensional scaffolds to regenerate cartilage, scaffold-free tissue engineering provides numerous advantages over scaffold-based methods. This review highlights the latest advancements in scaffold-free tissue engineering for cartilage and the potential for clinical translation. Impact statement Although scaffolds are often incorporated into cartilage tissue engineering strategies as a three-dimensional architecture conducive to tissue formation, scaffold-free approaches are increasingly recognized for their ability to better recapitulate the native tissue formation process. Recent advancements in scaffold-free tissue engineering and success in clinical trials demonstrate the potential of these techniques to serve as viable therapies for repairing and restoring damaged cartilage.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".