Emerging Therapeutic Modalities and Pharmacotherapies in Neuropathic Pain Management: A Systematic Review and Meta‐Analysis of Parallel Randomized Controlled Trials
Bibliographic record
Abstract
Background: Neuropathic pain (NP) is a chronic condition caused by abnormal neuronal excitability in the nervous system. Current treatments for NP are often ineffective or poorly tolerated. Hence, we reviewed the efficacy and safety of novel drugs or devices that target neuronal excitability in NP patients compared with placebo, sham, or usual care interventions. Methods: Six databases were searched for parallel randomized controlled trials (RCTs) reporting novel devices (rTMS, SCS, and TENS) or drugs (EMA401, capsaicin 8% patch, and Sativex) for NP. Data were extracted and quality was assessed using the ROB2 tool. The random‐effects inverse variance method was used for analysis. Results: In our review of 30 RCTs with 4251 participants, device‐based interventions were found to be more effective in reducing pain scores than control interventions (SMD = −1.27, 95% CI: −1.92 to −0.62). However, high heterogeneity was seen ( p < 0.01, I 2 = 91%), attributable to the etiology of NP ( R 2 = 58.84%) and year of publication ( R 2 = 49.49%). Funding source and type of control comparator were ruled out as cause of heterogeneity. Although drug interventions did not differ from placebo interventions in absolute pain reduction (SMD = −1.21, 95% CI: −3.55 to 1.13), when comparing relative change in pain intensity from baseline, drug interventions were found to be effective (SMD = 0.29, 95% CI: 0.04–0.55). Asymmetry in the funnel plot was visualized, suggesting publication bias. Certainty of evidence was very low according to GRADE assessment. Conclusions: Our review indicates that device‐based interventions are more effective than control interventions in reducing pain intensity in NP. Nevertheless, available evidence is limited due to heterogeneity and publication bias, prompting the need for more high‐quality RCTs to confirm the efficacy and safety of these interventions.
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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 | 0.162 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| 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".