An exploratory identification of biological markers of chronic musculoskeletal pain in the low back, neck, and shoulders
Bibliographic record
Abstract
OBJECTIVES: This study was an in-depth exploration of unique data from a nationally representative sample of adults living in the United States to identify biomarkers associated with musculoskeletal pain. METHODS: We performed secondary analyses of 2003-2004 NHANES data. After a first screening of 187 markers, analyses of 31 biomarkers were conducted on participants aged ≥20 years identified in all counties using the 2000 Census Bureau data (n = 4,742). To assess the association of each biomarker with each pain outcome (acute, subacute and chronic low back, neck, and shoulder pain), analyses were carried out using multivariable logistic regression with adjustments for sex, age and body mass index. Biomarkers were considered as continuous variables and categorized at the median of their distributions. RESULTS: Pain at any site for ≥24 hours during the past month was reported by 1,214 participants. Of these, 779 mentioned that the pain had lasted for ≥3 months ("chronic pain"). α-carotene, ascorbic acid, β-carotene, mercury and total protein had a statistically significant, inverse association with ≥2 chronic pain sites. Acrylamide, alkaline phosphatase, cadmium, cotinine, glycidamide, homocysteine, retinol, triglycerides and white blood cell count were positively associated with ≥2 chronic pain sites. Few biological markers were associated with acute and subacute pain. CONCLUSIONS: This study identified some biomarkers that were strongly and consistently associated with musculoskeletal pain. These results raise new hypotheses and could have tremendous implications for advancing knowledge in the field. Research on musculoskeletal pain needs to put more effort on the biological dimension of the biopsychosocial model of pain.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".