Epidemiology of Modic changes in dogs: Prevalence, possible risk factors, and association with spinal phenotypes
Bibliographic record
Abstract
Background: Chronic low back pain, a leading contributor to disease burden worldwide, is often caused by intervertebral disc (IVD) degeneration. Modic changes (MCs) are MRI signal intensity changes due to lesions in vertebral bone marrow adjacent to degenerated IVDs. Only a few studies described the histopathological changes associated with MC to date. MC type 1 is suggested to be associated with bone marrow infiltration of fibrovascular tissue, type 2 with fatty infiltration, and type 3 with bone sclerosis in humans. Methods: This study investigated whether the dog can be a valuable animal model to research MCs, by examining the prevalence, imaging, and histological characteristics of lumbar MCs in dogs (340 dogs, 2496 spinal segments). Results: Logistic regression analysis indicated that the presence of lumbosacral MCs was associated with age and disc herniation (annulus fibrosis protrusion and/or nucleus pulposus extrusion). According to MRI analysis, MCs were mostly detected at the lumbosacral junction in dogs. Most signal intensity changes represented MC type 3, while previous spinal surgery seemed to predispose for the development of MC type 1 and 2. Histological analysis (16 dogs, 39 spinal segments) indicated that IVDs with MCs showed more histopathological abnormalities in the endplate and vertebral bone marrow than IVDs without MCs. Mostly chondroid proliferation in the bone marrow was encountered, while the histologic anomalies described in humans associated with MCs, such as fibrovascular or fatty infiltration, were scarcely detected. Conclusions: Dogs spontaneously develop MCs, but may exhibit other pathological processes or more chronic bone marrow pathologies than humans with MCs. Therefore, more research is needed to determine the translatability of the MCs encountered in dog low-back-pain patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".