Effect of Interactive Neurostimulation Therapy on Inflammatory Response in Patients With Chronic and Recurrent Mechanical Neck Pain
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to evaluate the effect of treatment with a novel noninvasive interactive neurostimulation device (InterX5000) on the production of inflammatory biomarkers in chronic and recurrent mechanical neck pain (NP) syndrome. METHODS: This study represents pilot biological data from a randomized controlled clinical trial. Twenty-five NP patients and 14 asymptomatic subjects included for baseline comparison only completed the study. The patients received 6 InterX5000 or placebo treatments within 2 weeks, and pretreatment and post-treatment blood samples were collected for in vitro determination of biomarker production. Whole blood cell cultures were activated by lipopolysaccharide or by the combination of lipopolysaccharide and phytohemagglutinin for 24 to 48 hours. The levels of tumor necrosis factor α (TNFα) and its soluble type II receptor (sTNFR II), interleukin (IL) 1, IL-1 receptor antagonist (IL-1RA), IL-6, IL-10, and monocyte chemotactic protein (CCL2/MCP-1) were determined by specific immunoassays. RESULTS: Compared with asymptomatic subjects, baseline production levels of all proinflammatory mediators (TNFα, IL-1β, IL-6, and CCL2/MCP-1) were significantly augmented or trended higher (P = .000-.008) in patients with NP. Of the anti-inflammatory markers, only IL-1RA was significantly elevated (P = .004). The increase in IL-10 and tumor necrosis factor receptor II levels did not reach statistical significance. Neither InterX5000 nor placebo therapy had any significant effect on the production of the inflammatory mediators over the study period. CONCLUSION: This investigation determined that inflammatory cytokine pathways are activated in NP patients but found no evidence that a short course of InterX5000 treatment normalized the production of inflammatory biomarkers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".