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Record W7070653892

Relationship Between Paraspinal Muscle Morphology, Function, and Physical Status in Common Spinal Disorders

2024· dissertation· en· W7070653892 on OpenAlexfundno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2024
Typedissertation
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
FundersRUDN UniversityFonds de Recherche du Québec - SantéUniversità degli Studi di FirenzeUniversity of Oxford
KeywordsLow back painLumbarWeaknessMultifidus muscleMuscle weaknessMyelopathyMagnetic resonance imagingBack painSpinal manipulation
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.043
GPT teacher head0.342
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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