Do variations in paraspinal muscle morphology and composition predict low back pain in men?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it