Is there a difference between heat-capsaicin induced low back pain and placebo for neural oscillations and inflammatory blood markers? An experimental randomized crossover study
Bibliographic record
Abstract
Purpose: Low back pain is difficult to study due to its heterogeneity. Inducing back pain experimentally, with an established model such as heat-capsaicin, would beneficially control for some variability. How heat-capsaicin affects neurophysiological factors relevant to back pain is currently unknown, therefore, this study used a randomized crossover design with the aim to explore the differences between heat-capsaicin and placebo on brain activity and blood markers. Methods: 18 healthy participants completed two sessions: heat-capsaicin (45°C heat + capsaicin) and placebo (reduced heat + placebo). Pre- and post-pain-induction/placebo, electroencephalogram and blood draws were taken, and perceived pain was rated with a 100 m visual analog scale. Band power was calculated for theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma1 (30-58 Hz), and gamma2 (62-100 Hz) for six brain regions. An immune assay was run on plasma in duplicate for cytokines IL-1β, IL-6, IL-10, and TNFα. A repeated measures ANCOVA was run for all variables comparing between conditions (heat-capsaicin, placebo) with baseline measures as covariates. A Pearson's correlation was used to determine the relationship between perceived pain ratings and brain wave and blood biomarkers. Results: ≥ 0.15). Conclusion: Levels of perceived pain did not relate to neurophysiological changes that may occur immediately after heat-capsaicin exposure. Although changes have been found with other pain models and clinical low back pain, a statistically significant systematic response was not measurable using blood cytokine markers immediately after pain induction and may take longer to develop.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 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) | 0.000 | 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 teacher head, 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".