Antidepressants Are Effective in Decreasing Neuropathic Pain After SCI: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically review and assess the effectiveness and safety of antidepressants for neuropathic pain among individuals with spinal cord injury (SCI). METHODS: A systematic search was conducted using multiple databases for relevant articles published from 1980 to April 2014. Randomized controlled trials (RCTs) involving antidepressant treatment of neuropathic pain with ≥ 3 individuals and ≥ 50% of study population with SCI were included. Two independent reviewers selected studies based on inclusion criteria and then extracted data. Pooled analysis using Cohen's d to calculate standardized mean difference, standard error, and 95% confidence interval for primary (pain) and other secondary outcomes was conducted. RESULTS: Four RCTs met inclusion criteria. Of these, 2 studies assessed amitriptyline, 1 trazadone, and 1 duloxetine among individuals with neuropathic SCI pain. A small effect was seen in the effectiveness of antidepressants in decreasing pain among individuals with SCI (standardized mean difference = 0.34 ± 0.15; 95% CI, 0.05-0.62; P = .02). A number needed to treat of 3.4 for 30% or more pain relief was found by pooling 2 studies. Of these, significantly higher risk of experiencing constipation (risk ratio [RR] = 1.74; 95% CI, 1.09-2.78; P = .02) and dry mouth (RR = 1.39; 95% CI, 1.04-1.85; P = .02) was found amongst individuals receiving antidepressant treatment compared to those in the control group. CONCLUSIONS: The current meta-analysis demonstrates that antidepressants are effective in reducing neuropathic SCI pain. However, this should be interpreted with caution due to the limited number of studies. Further evaluation of long-term therapeutic options may be required.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".