Association Between the Plasma Levels of Mediators of Inflammation With Pain and Disability in the Elderly With Acute Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional study with subsample of elderly women with acute low back pain (LBP), from Back Complaints in the Elders-Brazil (BACE-Brazil) OBJECTIVE: To investigate the association between plasma levels of mediators of inflammation (interleukin-1 beta (IL-1β), IL-6, tumor necrosis factor alpha (TNF-α), and soluble TNF receptor 1 (sTNF-R1)) with pain and disability experienced by elderly women with acute LBP. SUMMARY OF BACKGROUND DATA: Among the elderly, LBP is a complaint of great importance and can lead to disability. Inflammatory cytokines are elevated in painful conditions, and may promote pain. METHODS: We included 155 community-dwelling elderly women (age ≥ 65 yr), who presented with a new (acute) episode of LBP. Enzyme-linked immunosorbent assays were used to measure TNF-α, sTNF-R1, IL-1β, and IL-6. Disability was assessed using the Roland Morris Disability Questionnaire; pain was assessed using the McGill Pain Questionnaire. Linear regression models were fit with each pain and disability outcome as dependent variables: Present Pain Intensity; Qualities of pain; Severity of pain in the last week; LBP frequency and disability. RESULTS: Depressive symptoms and IL-6 were associated and explained 20.9% of "qualities of pain" variability. TNF-α, sTNFR1, education, body mass index, and depressive symptoms explained 8.4% of "Severity of pain in the past week" variability. TNF-α, education, BMI, depressive symptoms, present pain intensity, qualities of pain, and LBP frequency explained 48.6% of "disability." No associations between inflammatory cytokines and "present pain intensity" and "LBP frequency" were found. CONCLUSION: Our results demonstrate associations between inflammatory markers (TNF-α and sTNFR1) and pain severity, IL-6 was associated with the qualities of pain, and TNF-α was also associated with disability. These inflammatory mediators represent new markers to be considered in the assessment and treatment of elderly patients with LBP. LEVEL OF EVIDENCE: 5.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".