Effect of low dose naltrexone for long covid: a systematic review
Bibliographic record
Abstract
Abstract Background Long covid is a debilitating chronic condition, and the effect of low dose naltrexone (LDN) on its symptoms is unclear. We aimed to determine the effectiveness of LDN on symptoms of long covid. Methods We searched PubMed, Embase, and Cochrane Library for published studies; ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform for registered ongoing studies from inception to 1 May 2025. Eligible studies were randomised controlled trials or pre-post studies in patients with long covid reporting on fatigue, quality of life, cognition, or other symptoms. Risk of bias was assessed by Newcastle-Ottawa scale. Results Of 226 titles and abstracts screened, no randomised controlled trials were identified. Four observational pre-post studies from USA and Ireland (n=155) met inclusion criteria. LDN doses varied from 1mg/d to 6 mg/d. Pooled analyses showed moderate effects for reducing fatigue (Hedges’ g= -0.74; 95% CI [-1.11, -0.37]; p<0.001), brain fog (Hedges’ g= - 0.53; 95%CI [-1.01, -0.05]; p=0.03), and improving sleep quality (Hedges’ g= -0.60; 95%CI [-0.91, -0.30]; p=0.0001), and large effects for pain (Hedges’ g= -0.93; 95%CI [-1.29, -0.57]; p<0.001) and daily functioning (Hedges’ g= -0.93; 95%CI [-1.29, -0.57]; p<0.0001) in favour of LDN. Heterogeneity ranged from 0% to 62%. Risk of bias was assessed as low in all four studies. No serious adverse events were reported in the two studies that assessed safety. Conclusion Limited evidence from small pre-post studies suggests LDN may improve fatigue, cognition, sleep, pain, and functioning in long covid. However, certainty of evidence is low. Well-powered trials are urgently needed to confirm efficacy, determine dosing and duration, and identify subgroups most likely to benefit. Protocol registration Open Science Framework https://doi.org/10.17605/OSF.IO/C2VKX
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".