Spinal degeneration and lumbar multifidus muscle quality may independently affect clinical outcomes in patients conservatively managed for low back or leg pain
Bibliographic record
Abstract
Few non-surgical, longitudinal studies have evaluated the relations between spinal degeneration, lumbar multifidus muscle (LMM) quality, and clinical outcomes. None have assessed the potential mediating role of the LMM between degenerative pathology and 12-month clinical outcomes. This prospective cohort study used baseline and 12-month follow-up data from 569 patients conservatively managed for low back or back-related leg pain to estimate the effects of aggregate degenerative lumbar MRI findings and LMM quality on 12-month low back and leg pain intensity (0-10) and disability (0-23) outcomes, and explored the mediating role of LMM quality between degenerative findings and 12-month clinical outcomes. Adjusted mixed effects generalized linear models separately estimated the effect of aggregate spinal pathology and LMM quality. Mediation models estimated the direct and indirect effects of pathology on leg pain, and pathology and LMM quality on leg pain, respectively. Multivariable analysis identified a leg pain rating change of 0.99 [0.14; 1.84] (unstandardized beta coefficients [95% CI]) in the presence of ≥ 4 pathologies, and a disability rating change of - 0.65 [- 0.14; - 1.16] for each 10% increase in muscle quality, but no effect on back pain intensity. Muscle quality had a non-significant mediating role (13.4%) between pathology and leg pain intensity. The number of different pathologies present demonstrated a small effect on 12-month leg pain intensity outcomes, while higher LMM quality had a direct effect on 12-month disability ratings but no mediating effect between pathology and leg pain. The relations between degenerative pathology, LMM quality, and pain-related outcomes appear complex and may include independent pathways.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".