3D Bioprinting for Cartilage and Osteochondral Tissue Engineering
Bibliographic record
Abstract
Significant progress has been made in the field of cartilage and bone tissue engineering over the last two decades. As a result, there is real promise that strategies to regenerate rather than replace damaged or diseased bones and joints will one day reach the clinic however, a number of major challenges must still be addressed before this becomes a reality. These include vascularization in the context of large bone defect repair, engineering complex gradients for bone-soft tissue interface regeneration and recapitulating the stratified zonal architecture present in many adult tissues such as articular cartilage. Tissue engineered constructs typically lack such spatial complexity in cell types and tissue organization, which may explain their relatively limited success to date. This has led to increased interest in bioprinting technologies in the field of musculoskeletal tissue engineering. The additive, layer by layer nature of such biofabrication strategies makes it possible to generate zonal distributions of cells, matrix and bioactive cues in 3D. The adoption of biofabrication technology in musculoskeletal tissue engineering may therefore make it possible to produce the next generation of biological implants capable of treating a range of conditions. Here, advances in bioprinting for cartilage and osteochondral tissue engineering are reviewed.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".