Urinary TNF-α as a potential biomarker for chronic primary low back pain
Bibliographic record
Abstract
Introduction: Over two thirds of individuals with low back pain (LBP) may experience recurrent or persistent symptoms in the long term. Yet, current data do not allow to predict who will develop chronic low back pain and who will recover from an acute episode. Elevated serum levels of the proinflammatory cytokine tumor necrosis factor-α (TNF-α) have been associated with poor recovery and persistent pain following an acute episode of LBP. Inflammatory cytokines may also mediate mechanisms involved in nociplastic pain, and thus, have significant implications in chronic primary low back pain (CPLBP). Methods: This study aimed to investigate the potential of urinary TNF-α levels for predicting outcomes and characterizing clinical features of CPLBP patients. Twenty-four patients with CPLBP and 24 sex- and age-matched asymptomatic controls were recruited. Urinary TNF-α concentrations were measured at baseline and after 4 weeks, during which CPLBP patients underwent spinal manipulative therapy (SMT). Results: Concentrations of TNF-α were found to be elevated in baseline urine samples of CPLBP patients compared to asymptomatic controls. Moreover, these values differed among patients depending on their pain trajectory. Patients with persistent pain showed higher levels of TNF-α, when compared to those with episodic CPLBP. Furthermore, baseline TNF-α concentrations and their changes after 4 weeks predicted alterations in pain intensity and disability following SMT in patients with CPLBP. Discussion: These findings warrant further research on the potential use of urinary TNF-α concentrations as a prognostic biomarker for CPLBP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".