Efficacy and Safety of N-Acetylcysteine for the Management of Chronic Pain in Adults: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy and safety of N-acetylcysteine in the treatment of chronic pain. METHODS: A systematic search was carried out until April 2020 for clinical studies of N-acetylcysteine in the management of any persistent or recurrent chronic pain condition for adults ≥ 18 years old. Risk of bias was assessed using the validated risk of bias tools. When appropriate, a meta-analysis using a random-effects model was performed, with a fixed-effect model for sensitivity analysis. RESULTS: Nine studies (n = 863) were included (five randomized controlled trials [RCTs], two open-label non-comparative studies and two comparative studies), that evaluated patients with sickle cell disease (3), complex regional pain syndrome (1), pelvic pain/endometriosis (2), rheumatoid arthritis (1), diabetic neuropathy (1), and chronic neuropathic pain (1). In the pooled analysis of three RCTs, N-acetylcysteine did not reduce pain intensities (SMD -0.21, 95% confidence interval [CI]: -0.33 to 0.75, random-effects), improve functional outcomes (SMD 0.21, 95% CI -0.33 to 0.75) or quality of life (SMD 0.60, 95% CI: -4.44 to 5.64); however, sensitivity analysis with a fixed effect model demonstrated an effect for pain intensities and function. Due to adverse events being inconsistently reported, no conclusion could be made regarding safety of N-acetylcysteine in chronic pain. CONCLUSIONS: While there is some evidence to indicate N-acetylcysteine may provide analgesic efficacy for certain pain conditions, there is insufficient evidence to provide definitive evidence on NAC in chronic pain management. Larger-size RCTs spanning a variety of chronic pain conditions are needed to determine N-acetylcysteine's role, if any, in pain medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".