Do variations in paraspinal muscle morphology and composition predict low back pain in men?
Bibliographic record
Abstract
This longitudinal study aimed to clarify the longstanding controversy over whether variations in paraspinal muscle morphology (e.g., size, composition and asymmetry) are predictors of low back pain (LBP). A sample of 99 Finnish men were included in this population-based longitudinal study. Data were collected through a structured interview, physical examination and magnetic resonance imaging (MRI). Baseline measurements of the lumbar multifidus and erector spinae muscles were obtained from T2-weighted axial images at L3-L4 and L5-S1, and interview data were obtained at baseline, 1- and 15-year follow-ups. Few of the paraspinal muscle parameters investigated were predictors of change in LBP frequency, intensity or sciatica at 1- and 15-year follow-ups in the population-based sample, and findings were not consistent across muscles and spinal levels. However, greater multifidus and erector spinae fatty infiltration at L5-S1 was associated with a higher risk of having continued, frequent, persistent LBP at 1-year follow-up. None of the relationships observed was confounded by body mass index or the amount of physical activity at work or leisure. This longitudinal study provided evidence that variations in paraspinal muscle morphology on MRI have a limited, if not uncertain, role in the short- and long-term predictions of LBP in men.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".