Animal models used in meniscal repair research from ex vivo to in vivo: A systematic review
Bibliographic record
Abstract
This systematic review, registered with Prospero, aims to identify an optimal animal model for meniscus repair research, moving from ex vivo experimentation to in vivo studies. Data sources included PubMed, Medline, all Evidence-Based Medicine Reviews, Web of Science, and Embase searched in March 2023. Studies were screened using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extracted data including animal model, type of experiment, type of tear, surgical techniques, and measured outcomes, were recorded, reviewed, and analyzed by four independent reviewers. The SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) Rob tool was used for critical appraisal and risk of bias assessment. Out of 11,719 studies, 72 manuscripts were included for data extraction and analysis; 41 ex vivo extra-articular studies, 20 ex vivo intra-articular studies, and only 11 in vivo studies. Six animal models were employed: porcine, bovine, lapine, caprine, canine, and ovine. Longitudinal lesions were the most frequently studied tear pattern and sutures the most common repair technique. Studied outcomes focused mainly on biomechanical assessments and gross observations. This systematic review can guide researchers in their choice of animal model for meniscus repair research; it highlighted the strengths of the porcine, caprine, and bovine models for ex vivo cadaveric studies, while the porcine and caprine models were found to be more suited to in vivo studies due to their similarities with human anatomy. Research teams should familiarize themselves with the advantages and disadvantages of various animal models before initiating protocols to improve standardization in the field.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".