Evidence for an association of serum microanalytes and myofascial pain syndrome
Bibliographic record
Abstract
BACKGROUND: Myofascial Pain Syndrome (MPS) is a common pain disorder. Diagnostic criteria include physical findings which are often unreliable or not universally accepted. A precise biosignature may improve diagnosis and treatment effectiveness. The purpose of this study was to assess whether microanalytic assays significantly correlate with characteristic clinical findings in people with MPS. METHODS: This descriptive, prospective study included 38 participants (25 women) with greater than 3 months of myofascial pain in the upper trapezius. Assessments were performed at a university laboratory. The main outcome measures were the Beighton Index, shoulder range of motion, strength asymmetries and microanalytes: DHEA, Kynurenine, VEGF, interleukins (IL-1b, IL-2, IL-4, IL-5, IL-7, IL-8, IL-13), growth factors (IGF-1, IGF2, G-CSF, GM-CSF), MCP-1, MIP-1b, BDNF, Dopamine, Noradrenaline, NPY, and Acetylcholine. Mann-Whitney test and Spearman's multivariate correlation were applied for all variables. The Spearman's analysis results were used to generate a standard correlation matrix and heat map matrix. RESULTS: Mean age of participants was 32 years (20-61). Eight (21%) had widespread pain (Widespread Pain Index ≥ 7). Thirteen (34%) had MPS for 1-3 years, 14 (37%) 3-10 years, and 11 (29%) for > 10 years. The following showed strong correlations: IL1b,2,4,5,7,8; GM-CSF and IL 2,4,5,7; between DHEA and BDNF and between BDNF and Kynurenine, NPY and acetylcholine. The heat map analysis demonstrated strong correlations between the Beighton Index and IL 5,7, GM-CSF, DHEA. Asymmetries of shoulder and cervical spine motion and strength associated with select microanalytes. CONCLUSION: Cytokine levels significantly correlate with selected clinical assessments. This indirectly suggests possible biological relevance for understanding MPS. Correlations among some cytokine clusters; and DHEA, BDNF kynurenine, NPY, and acetylcholine may act together in MPS. These findings should be further investigated for confirmation that link these microanalytes with select clinical findings in people with MPS.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".