Associations Between Back Pain History and Lumbar MRI Findings
Bibliographic record
Abstract
STUDY DESIGN: Retrospective monozygotic twin cohort study. OBJECTIVES: Our goal was to investigate the associations between different spinal MRI findings and current, past year, and lifetime low back pain after adjusting for occupational physical loading, smoking, genetics, and early family influences. SUMMARY OF BACKGROUND DATA: The role of spinal pathology in back symptoms continues to be controversial. METHODS: The study participants consisted of 115 monozygotic male twin pairs 35 to 69 years of age. The qualitatively assessed MRI parameters were as follows: disc height, bulging, herniations, anular tears, osteophytes, spinal stenosis, and endplate changes. Signal intensity was measured quantitatively. RESULTS: After controlling for age, disc height was associated with all back pain variables studied and anular tears with LBP frequency and intensity during the 12 months before imaging. Both were associated with lifetime frequency of low back pain interfering with daily activities, disability, and intensity of the worst lifetime pain episode. Other MRI findings did not explain the various symptom histories. Adjusting for physical loading in the past 12 months increased the associations of anular tears and "low back pain today" and 12-month low back pain parameters. After controlling for genotype and other familial influences, the within-pair differences in disc height and anular tears accounted for 6% to 12% of the total variance in the within-pair differences of low back pain variables. CONCLUSION: These findings raise new questions about the underlying mechanisms of LBP. The sensitivities of the only significant MRI parameters, disc height narrowing and anular tears, are poor, and these findings alone are of limited clinical importance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".