Systemic inflammatory markers in neck pain: A systematic review with meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Mechanisms underpinning symptoms in non-traumatic neck pain (NTNP) and whiplash-associated disorder (WAD) are not comprehensively understood. There is emerging evidence of systemic inflammation in musculoskeletal pain conditions, including neck and back pain. The aim of this systematic review was to determine if raised blood inflammatory markers are associated with neck pain. DATABASES AND DATA TREATMENT: MEDLINE, EMBASE, Cochrane Library, CINAHL and Web of Science databases were searched. Two independent reviewers identified studies for inclusion and extracted data. Meta-analysis was performed by random effects model to calculate standard mean differences (SMDs). Risk of bias of individual studies was assessed using the Newcastle-Ottawa Scale. Overall quality of evidence from meta-analysis was assessed by Grades of Recommendation, Assessment, Development and Evaluation approach. RESULTS: = 45%) in chronic neck pain compared to controls, but no increase in monocyte chemoattractant protein-1. Some inflammatory markers were associated with clinical variables (including pain intensity and disability). Quality of evidence was mostly low due to small samples and high heterogeneity. CONCLUSIONS: Findings imply that raised blood inflammatory markers are present in chronic neck pain, which may represent an ongoing inflammatory process in this population. SIGNIFICANCE: This systematic review advances our understanding of neck pain pathophysiology by demonstrating the presence of systemic inflammation in chronic neck pain, in the form of raised IL-1β and TNFα. Further, numerous inflammatory markers were associated with clinical variables, including pain intensity, disability and hyperalgesia. These findings imply that systemic inflammation may contribute to mechanisms underlying neck 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".