Preclinical Studies for Cartilage Repair
Bibliographic record
Abstract
Investigational devices for articular cartilage repair or replacement are considered to be significant risk devices by regulatory bodies. Therefore animal models are needed to provide proof of efficacy and safety prior to clinical testing. The financial commitment and regulatory steps needed to bring a new technology to clinical use can be major obstacles, so the implementation of highly predictive animal models is a pressing issue. Until recently, a reductionist approach using acute chondral defects in immature laboratory species, particularly the rabbit, was considered adequate; however, if successful and timely translation from animal models to regulatory approval and clinical use is the goal, a step-wise development using laboratory animals for screening and early development work followed by larger species such as the goat, sheep and horse for late development and pivotal studies is recommended. Such animals must have fully organized and mature cartilage. Both acute and chronic chondral defects can be used but the later are more like the lesions found in patients and may be more predictive. Quantitative and qualitative outcome measures such as macroscopic appearance, histology, biochemistry, functional imaging, and biomechanical testing of cartilage, provide reliable data to support investment decisions and subsequent applications to regulatory bodies for clinical trials. No one model or species can be considered ideal for pivotal studies, but the larger animal species are recommended for pivotal studies. Larger species such as the horse, goat and pig also allow arthroscopic delivery, and press-fit or sutured implant fixation in thick cartilage as well as second look arthroscopies and biopsy procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.026 |
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".