Systematic Comparison of Biomaterials‐Based Strategies for Osteochondral and Chondral Repair in Large Animal Models
Bibliographic record
Abstract
Joint repair remains a major challenge in orthopaedics. Recent progress in biomaterial design has led to the fabrication of a plethora of promising devices. Pre-clinical testing of any joint repair strategy typically requires the use of large animal models (e.g., sheep, goat, pig or horse). Despite the key role of such models in clinical translation, there is still a lack of consensus regarding optimal experimental design, making it difficult to draw conclusions on their efficacy. In this context, the authors performed a systematic literature review and a risk of bias assessment on large animal models published between 2010 and 2020, to identify key experimental parameters that significantly affect the biomaterial therapeutic outcome and clinical translation potential (including defect localization, animal age/maturity, selection of controls, cell-free versus cell-laden). They determined that mechanically strong biomaterials perform better at the femoral condyles; while highlighted the importance of including native tissue controls to better evaluate the quality of the newly formed tissue. Finally, in cell-laded biomaterials, the pre-culture conditions played a more important role in defect repair than the cell type. In summary, here they present a systematic evaluation on how the experimental design of preclinical models influences biomaterial-based therapeutic outcomes in joint repair.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".