Low–dose naltrexone for Long COVID
Bibliographic record
Abstract
In August 2024, the number of people impacted by Long COVID (LC) was estimated to be about 400 million globally (Al-Aly et al., 2024). Currently, there are no FDA–approved treatments for LC. Many of the centres treating LC have noticed the similarity to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in some patients. Low–dose naltrexone (LDN) is a drug that has recently shown potential in treating ME/CFS. In this integrative review, ten articles were selected for analysis to investigate whether LDN could be a viable treatment for LC. Although the studies conducted on LDN and LC are small, non–randomized, and unblinded, a few interesting themes emerged from this analysis that could guide future studies and treatment decisions. LDN seems to be most effective for individuals with a LC phenotype that mimics ME/CFS, as, of all the symptoms of LC, it was found to be most effective for fatigue and pain. In the future, pre–screening tools will likely be developed to identify patients most likely to respond to LDN. Two double–blind randomized controlled trials (RCTs) are currently underway that will be published next year, yielding a higher degree of evidence and certainty around LDN for LC. In the meantime, initial findings support consideration of LDN for patients with LC whose primary complaints are fatigue or pain.,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.019 | 0.002 |
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".