Clinical trials of N-acetylcysteine in psychiatry and neurology: A systematic review
Bibliographic record
Abstract
N-acetylcysteine (NAC) is recognized for its role in acetaminophen overdose and as a mucolytic. Over the past decade, there has been growing evidence for the use of NAC in treating psychiatric and neurological disorders, considering its role in attenuating pathophysiological processes associated with these disorders, including oxidative stress, apoptosis, mitochondrial dysfunction, neuroinflammation and glutamate and dopamine dysregulation. In this systematic review we find favorable evidence for the use of NAC in several psychiatric and neurological disorders, particularly autism, Alzheimer's disease, cocaine and cannabis addiction, bipolar disorder, depression, trichotillomania, nail biting, skin picking, obsessive-compulsive disorder, schizophrenia, drug-induced neuropathy and progressive myoclonic epilepsy. Disorders such as anxiety, attention deficit hyperactivity disorder and mild traumatic brain injury have preliminary evidence and require larger confirmatory studies while current evidence does not support the use of NAC in gambling, methamphetamine and nicotine addictions and amyotrophic lateral sclerosis. Overall, NAC treatment appears to be safe and tolerable. Further well designed, larger controlled trials are needed for specific psychiatric and neurological disorders where the evidence is favorable.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".