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Record W4405266087 · doi:10.31219/osf.io/9jhcq

Effects of Physical Exercise Training on Chronic Low Back Pain are Mediated by Changes in Frontostriatal Connectivity and Gene Expression

2024· preprint· en· W4405266087 on OpenAlexfundno aff
Scott J. Thompson, Mathieu Roy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchRéseau québécois de recherche sur la douleurLouise and Alan Edwards Foundation
KeywordsNucleus accumbensChronic painPrefrontal cortexExacerbationPhysical medicine and rehabilitationMedicinePhysical exerciseNeurosciencePsychologyImmune systemPhysical therapyCentral nervous systemInternal medicineCognitionImmunology

Abstract

fetched live from OpenAlex

Chronic low back pain is a prevalent and debilitating condition that can be difficult to resolve. One effective approach is physical exercise training. This study investigates the hypothesis that exercise training normalizes immune-brain interactions that cause pain exacerbation and perpetuation. Fifty-seven participants with chronic low back pain were randomized into a 14-week exercise training program or a wait-list control condition. Exercise training led to significant reductions in pain and disability. Brain imaging analyses revealed decreased nucleus accumbens and medial prefrontal cortex (NAc-mPFC) connectivity, suggesting a normalization of reward circuitry. Gene expression analysis in immune cells indicated that exercise training produced changes in biological pathways related to immune function, stress, and inflammation. In alignment with the hypothesis, the observed changes in NAc-mPFC connectivity and gene expression were found to mediate the effects of exercise training on chronic low back pain.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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