Paraspinal muscle pathophysiology associated with low back pain and spine degenerative disorders
Bibliographic record
Abstract
Low back pain disorders affect more than 80% of adults in their lifetime and are the leading cause of global disability. The muscles attaching to the spine (ie, paraspinal muscles) are critical for proper spine health and play a crucial role in the functioning of the spine and whole body; however, reports of muscle dysfunction and insufficiency in chronic LBP (CLBP) patients are common. This article presents a review of the current understanding of the relationship between paraspinal muscle pathophysiology and spine-related disorders. Human literature demonstrates a clear association between altered muscle structure/function, most notably fatty infiltration and fibrosis, and low back pain disorders; other associations, including muscle cell atrophy and fiber type changes, are less clear. Animal literature then provides some mechanistic insight into the complex relationships, including initiating factors and time courses, between the spine and spine muscles under pathological conditions. It is apparent that spine pathology can directly lead to changes in the paraspinal muscle structure, function, and biology. It also appears that changes to the muscle structure and function can directly lead to changes in the spine (eg, deformity); however, this relationship is less well studied. Future work must focus on providing insight into possible mechanisms that regulate spine and paraspinal muscle health, as well as probing how muscle degeneration/dysfunction might be an initiating factor in the progression of spine pathology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".