Cartilaginous Endplate Damage May be a Root Pathology Underlying Modic Changes on Lumbar Spine MR Images
Bibliographic record
Abstract
Study Design Rabbit model study. Objective To examine whether cartilaginous endplate (CEP) destruction leads to endplate and vertebral marrow signal changes (Modic changes, MCs) on MR images. Methods Sixteen adult rabbits were used in the study and underwent an anterolateral procedure to expose the L2-6 intervertebral discs. The L4/5, L3/4 and L2/3 discs underwent annulotomy, annulotomy and CEP curettage, or annulotomy and chemonucleolysis, respectively, while the L5/6 disc served as a shame control. MR imaging was performed preoperatively and at 1, 3, and 6 months postoperatively to evaluate the presence or absence of MCs. After the last imaging, animals were sacrificed for histological study, focusing on endplate pathologies and their associations with MCs. Results Among the 64 endplates that underwent CEP curettage or were exposed to chemonucleolysis, there were 6 (9.4%), 19 (29.7%), and 32 (50%) endplates with MCs at 1, 3 and 6 months, respectively. No MCs developed in the sham controls. Both surgical curettage and chymopapain injection successfully induced CEP destruction. Endplates with full layer CEP defects were most likely to develop MCs (59.6% vs 11.4%, P < 0.001). Moreover, endplates with MCs had a greater histological degeneration score than those without (8.97 ± 1.92 vs 5.35 ± 2.28, P < 0.001) and higher expression levels of inflammatory factors (IL-1β, TNF-α, and IL-6, P < 0.05 for all) in the subchondral vertebral marrow. Conclusions CEP destruction, induced either by physical curettage or chemical lysis, can lead to long-lasting inflammation in the vertebral marrow and Modic-like signal changes on MR images. CEP destruction may be a root pathology underlying MCs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".