Controversies in spine research: Organ culture versus in vivo models for studies of the intervertebral disc
Bibliographic record
Abstract
Intervertebral disc degeneration is a common cause of low back pain, the leading cause of disability worldwide. Appropriate preclinical models for intervertebral disc research are essential to achieving a better understanding of underlying pathophysiology and for the development, evaluation, and translation of more effective treatments. To this end, in vivo animal and ex vivo organ culture models are both widely used by spine researchers; however, the relative strengths and weaknesses of these two approaches are a source of ongoing controversy. In this article, members from the Spine and Preclinical Models Sections of the Orthopedic Research Society, including experts in both basic and translational spine research, present contrasting arguments in support of in vivo animal models versus ex vivo organ culture models for studies of the disc, supported by a comprehensive review of the relevant literature. The objective is to provide a deeper understanding of the respective advantages and limitations of these approaches, and advance the field toward a consensus with respect to appropriate model selection and implementation. We conclude that complementary use of several model types and leveraging the unique advantages of each is likely to result in the highest impact research in most instances.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".