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 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 | 0.006 | 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 | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".