Relationship Between Paraspinal Muscle Morphology, Function, and Physical Status in Common Spinal Disorders
Bibliographic record
Abstract
The deep paraspinal muscles are essential for providing physical support and stability to the spinal column. They play a vital role in maintaining fine postural control of the spine and are responsible for controlling all movements of the vertebral column. These muscles work in coordination to ensure proper alignment and movement of the spine, thereby contributing to overall spinal health and function. Dysfunction or weakness in paraspinal muscles can lead to instability, poor posture, and increased risk of spinal pain disorders. Therefore, understanding the role of deep paraspinal muscles is crucial in maintaining spinal health and preventing musculoskeletal disorders. This summary highlights the significance of assessing both morphology and function of paraspinal muscles in common spinal disorders including chronic low back pain (LBP) and degenerative cervical myelopathy (DCM). While previous studies have focused on either morphology or functional deficits separately, this dissertation aims to comprehensively investigate the structure-function relationship using advanced imaging techniques like magnetic resonance imaging (MRI) and ultrasound. Specifically, chapter three focuses on understanding the relationship between lumbar multifidus muscle (MF) muscle morphology and function in chronic LBP patients, utilizing measures such as fatty infiltration, contraction, stiffness, and elasticity. Similarly, chapter four and five aim to assess cervical muscle morphology as predictors of prognosis and functional recovery in patients with DCM, both pre- and post-operatively. Such comprehensive evaluations are crucial for improving diagnosis, intervention, and therapeutic strategies in spinal disorders, ultimately enhancing patients’ clinical outcomes and quality of life. Finally, chapter six discusses the findings from chapters three, four and five and offers a general conclusion and recommendations for future research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".