The Influence of Chronic Inflammation on Peripheral Motor Nerve Conduction Following Spinal Cord Injury: A Randomized Clinical Trial
Bibliographic record
Abstract
Objective: To examine the potential influence of chronic inflammation on peripheral motor nerve function in vivo following spinal cord injury (SCI). Methods: This study was part of a randomized, parallel-group, controlled clinical trial. The study included 20 participants with varying levels and severities of SCI randomized (3:2) to either a treatment group, consisting of a 12-week anti-inflammatory diet program, or control group. Outcome measures were assessed at baseline, 1 month, and 3 months and consisted of measures of motor nerve conduction velocity (NCV) and amplitude as well as markers of inflammation as assessed by various pro- and anti-inflammatory cytokines. Results: Despite a significant reduction in inflammation in the treatment group, 2-way repeated measures analysis of variance (ANOVA) showed no significant Group × Time interaction for motor NCV (p = .77) or M-wave amplitude (p = .61). Further, the change in motor NCV and M-wave amplitude were not shown to be associated with the change in inflammatory mediators as assessed via a backwards elimination multiple regression analysis. Conclusion: These results suggest that at physiologically relevant concentrations, inflammatory mediators may not have a substantial influence on peripheral motor nerve conduction in vivo following SCI. Future studies may still be warranted to examine the potential for central effects.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".