Association between MRI findings and inflammatory symptoms in non-specific chronic low back pain
Bibliographic record
Abstract
PURPOSE: Non-specific chronic low back pain (nsCLBP) likely constitutes a heterogeneous group of conditions, and identifying an inflammatory phenotype may improve treatment stratification. The objective of this study was to determine associations between inflammatory back pain (IBP) features and MRI findings in subjects with nsCLBP. METHODS: Participants were selected from the longitudinal Clinical Cohort for Comprehensive Deep Phenotyping of Chronic Low-Back Pain Adults Study (comeBACK), a cohort of adults with nsCLBP. IBP features (morning stiffness, nocturnal LBP, symptom improvement with exercise / worsening with rest, insidious onset, onset < 40 years) were assessed via questionnaire. MRI scans of the lumbar spine were interpreted by a radiologist, using a comprehensive scoring system: Modic changes (MC), endplate erosion, facet joint arthritis, central canal stenosis and degeneration of the sacroiliac joints (SIJ). Logistic regression was performed (presence IBP feature as dependent variable and MRI findings as independent variables). RESULTS: A total of 290 individuals (159 female) were included. Both endplate erosion and MC1 changes were positively associated with overall IBP (erosion: OR 2.1, 95%CI 1.3–3.3; MC1: OR 2.2, 95%CI 1.4–3.4), and specifically morning stiffness and worsening with rest. Negative association with overall IBP symptoms was found for SIJ degeneration (OR 0.6, 95%CI 0.4-1.0) and no association for facet arthropathy, MC2 and central canal stenosis. CONCLUSION: Our exploratory analysis supports the notion of an inflammatory nsCLBP phenotype with distinguishing imaging features, by establishing associations between endplate erosion and Modic type 1 changes with select IBP features.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".