Do Muscle Characteristics on Lumbar Spine Magnetic Resonance Imaging or Computed Tomography Predict Future Low Back Pain, Physical Function, or Performance? A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To determine whether lumbar muscle characteristics on magnetic resonance imaging (MRI) or computed tomography (CT) can inform clinicians as to the course of future low back pain (LBP), functional limitations, or physical performance, in adults with or without LBP. TYPE: Systematic review. LITERATURE REVIEW: We searched PubMed, Embase, and CINAHL through October 2014 for articles published in English in which authors assessed lumbar muscle characteristics on conventional MRI/CT as predictors of future LBP, functional limitations, or physical performance in adults. Studies with only postsurgical subjects were excluded. Our search identified 3554 articles, of which 6 observational cohort studies were included in the final review. METHODOLOGY: We used the Newcastle Ottawa Scale to evaluate potential bias. Data were extracted on study design, study population, sample size, participant characteristics, details of MRI/CT assessments, interventions, study outcomes, analysis methods, and study results. Because of heterogeneity between studies, we conducted a qualitative evidence synthesis. SYNTHESIS: Among high-quality studies, there was limited evidence that, for individuals with or without LBP, greater MRI-detected multifidus cross-sectional area at L5-S1 predicted greater LBP intensity at 1-year follow-up, lesser erector spinae fat infiltration (FI) at L5-S1 predicted greater LBP intensity at 15-year follow-up, and greater erector spinae side-to-side FI asymmetry at L3-L4 predicted lower LBP frequency at 15-year follow-up; however, there was also limited evidence that all other MRI-detected paraspinal muscle characteristics examined were not predictive of LBP incidence, prevalence, frequency, or intensity at follow-up durations ranging from 1 to 15 years. There was limited evidence that greater CT-detected trunk muscle FI predicted worse physical performance in older adults at 3-year follow-up, but that trunk muscle cross-sectional area did not. CONCLUSION: Few lumbar muscle characteristics have limited evidence for an association with future LBP and physical performance outcomes, and the vast majority have limited evidence for having no association with such outcomes.
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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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".