Targeting inflammation as a treatment modality for neuropathic pain in spinal cord injury: a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: The purpose of the present study was to examine the effectiveness of an anti-inflammatory intervention as a treatment for neuropathic pain following spinal cord injury (SCI). METHODS: This randomized, parallel-group, controlled clinical trial (NCT02099890) examined 20 participants with varying levels and severities of SCI, randomized (3:2) to either a 12-week anti-inflammatory diet, or control group. Outcome measures consisted of self-determined indices of pain as assessed using the neuropathic pain questionnaire (NPQ) and markers of inflammation as assessed by various pro- and anti-inflammatory cytokines, as well as the eicosanoids PGE2 and LTB4. RESULTS: A significant group × time interaction was found for sensory pain scores (p < 0.01). A Mann-Whitney test revealed that the change scores (3-month baseline) were significantly different between groups for IFN-y (U = 13.0, p = 0.01), IL-1β (U = 14.0, p = 0.01), and IL-2 (U = 12.0, p = 0.01). A Friedman test revealed the treatment group had a significant reduction in IFN-y (x (2) = 8.67, p = 0.01), IL-1β (x (2) = 17.78, p < 0.01), IL-6 (x (2) = 6.17, p < 0.05), while the control group showed no significant change in any inflammatory mediator. A stepwise backward elimination multiple regression analysis showed that the change in sensory neuropathic pain was a function of the change in the proinflammatory cytokines IL-2 and IFN-y, as well as the eicosanoid PGE2 (R = 0.689, R (2) = 0.474). CONCLUSIONS: Overall, the results of the study demonstrate the efficacy of targeting inflammation as a means of treating neuropathic pain in SCI, with a potential mechanism relating to the reduction in proinflammatory cytokines and PGE2. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02099890.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".