Effects of Distinct Force Magnitude of Spinal Manipulative Therapy on Blood Biomarkers of Inflammation: A Proof of Principle Study in Healthy Young Adults
Bibliographic record
Abstract
OBJECTIVES: The purpose of this preliminary study was to determine the influence of thoracic spinal manipulation therapy (SMT) of different force magnitudes on blood biomarkers of inflammation in healthy adults. METHODS: Nineteen healthy young adults (10 female, age: 25.6 ± 1.2 years) were randomized into the following 3 groups: (1) control (preload only), (2) single thoracic SMT with a total peak force of 400N, and (3) single thoracic SMT with a total peak force of 800N. SMT was performed by an experienced chiropractor, and a force-plate embedded treatment table (Force Sensing Table Technology) was used to determine the SMT force magnitudes applied. Blood samples were collected at pre intervention (baseline), immediately post intervention, and 20 minutes post intervention. A laboratory panel of 14 different inflammatory biomarkers (pro, anti, dual role, chemokine, and growth factor) was assessed by multiplex array. Change scores from baseline of each biomarker was used for statistical analysis. Two-way repeated-measures analysis of variance was used to investigate the interaction and main effects of intervention and time on cytokines, followed by Tukey's multiple comparison test (P ≤ .05). RESULTS: A between-group (800N vs 400N) difference was observed on interferon-gamma, interleukin (IL)-5, and IL-6, while a within-group difference (800N: immediately vs 20 minutes post-intervention) was observed on IL-6 only. CONCLUSION: In this study, we measured short-term changes in plasma cytokines in healthy young adults and found that select plasma pro-inflammatory and dual-role cytokines were elevated by higher compared to lower SMT force. Our findings aid to advance our understanding of the potential relationship between SMT force magnitude and blood cytokines and provide a healthy baseline group with which to compare similar studies in clinical populations in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".