Strategic Design and Fabrication of Engineered Scaffolds for Articular Cartilage Repair
Bibliographic record
Abstract
Damage to articular cartilage can eventually lead to osteoarthritis (OA), a debilitating, degenerative joint disease that affects millions of people around the world. The limited natural healing ability of cartilage and the limitations of currently available therapies make treatment of cartilage defects a challenging clinical issue. Hopes have been raised for the repair of articular cartilage with the help of supportive structures, called scaffolds, created through tissue engineering (TE). Over the past two decades, different designs and fabrication techniques have been investigated for developing TE scaffolds suitable for the construction of transplantable artificial cartilage tissue substitutes. Advances in fabrication technologies now enable the strategic design of scaffolds with complex, biomimetic structures and properties. In particular, scaffolds with hybrid and/or biomimetic zonal designs have recently been developed for cartilage tissue engineering applications. This paper reviews critical aspects of the design of engineered scaffolds for articular cartilage repair as well as the available advanced fabrication techniques. In addition, recent studies on the design of hybrid and zonal scaffolds for use in cartilage tissue repair are highlighted.
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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) | 0.000 | 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 | 0.000 |
| 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".