A comprehensive tool box for large animal studies of intervertebral disc degeneration
Bibliographic record
Abstract
Preclinical studies involving large animal models aim to recapitulate the clinical situation as much as possible and bridge the gap from benchtop to bedside. To date, studies investigating intervertebral disc (IVD) degeneration and regeneration in large animal models have utilized a wide spectrum of methodologies for outcome evaluation. This paper aims to consolidate available knowledge, expertise, and experience in large animal preclinical models of IVD degeneration to create a comprehensive tool box of anatomical and functional outcomes. Herein, we present a Large Animal IVD Scoring Algorithm based on three scales: macroscopic (gross morphology, imaging, and biomechanics), microscopic (histological, biochemical, and biomolecular analyses), and clinical (neurologic state, mobility, and pain). The proposed algorithm encompasses a stepwise evaluation on all three scales, including spinal pain assessment, and relevant structural and functional components of IVD health and disease. This comprehensive tool box was designed for four commonly used preclinical large animal models (dog, pig, goat, and sheep) in order to facilitate standardization and applicability. Furthermore, it is intended to facilitate comparison across studies while discerning relevant differences between species within the context of outcomes with the goal to enhance veterinary clinical relevance as well. Current major challenges in pre-clinical large animal models for IVD regeneration are highlighted and insights into future directions that may improve the understanding of the underlying pathologies are discussed. As such, the IVD research community can deepen its exploration of the molecular, cellular, structural, and biomechanical changes that occur with IVD degeneration and regeneration, paving the path for clinically relevant therapeutic strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".