A systematic review of the role of inflammatory biomarkers in acute, subacute and chronic non-specific low back pain
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is one of the greatest contributors to disability in the world and there is growing interest on the role of biomarkers in LBP. To purpose of this review was to analyze available evidence on the relationship between inflammatory biomarkers, clinical presentation, and outcomes in patients with acute, subacute and chronic non-specific low back pain (NSLBP). METHODS: A search was performed in Medline, Embase, Cinahl and Amed databases. Studies which measured levels of inflammatory biomarkers in participants with NSLBP were included. Two reviewers independently screened titles and abstracts, full-texts, and extracted data from included studies. Methodological quality was assessed using the Newcastle Ottawa Quality Assessment Scale. Level of evidence was assessed using the modified GRADE approach for prognostic studies. RESULTS: Seven primary studies were included in this review. All results assessed using the modified GRADE demonstrated low to very low quality evidence given the small number of studies and small sample. Three studies examined C-reactive protein (CRP), one of which found significantly higher CRP levels in an acute NSLBP group than in controls and an association between high pain intensity and elevated CRP. Three studies examined tumor necrosis factor alpha (TNF-α), two of which found elevated TNF-α in chronic NSLBP participants compared to controls. Two studies examined interleukin 6 (IL-6), none of which found a significant difference in IL-6 levels between NSLBP groups and controls. Two studies examined interleukin 1 beta (IL-β), none of which found a significant difference in IL-β levels between NSLBP groups and controls. CONCLUSIONS: This review found evidence of elevated CRP in individuals with acute NSLBP and elevated TNF-Α in individuals with chronic NSLBP. There are a limited number of high-quality studies evaluating similar patient groups and similar biomarkers, which limits the conclusion of this review.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".