ReviewJournal of general internal medicine2025
Interventions for Long COVID: A Narrative Review.
Review in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- COVID-19 infection prior to the onset of type 1 diabetes does not impair beta-cell function: a two-year nationwide follow-up study.BMC endocrine disorders · 2026Observational
- Definition, Symptoms, Risk Factors, Epidemiology, and Autoimmunity of Long COVID.American journal of medicine open · 2026Article
- Cost-effectiveness of a brief cognitive-behavioral outpatient rehabilitation program for patients with post-COVID-19 condition within a pragmatic randomized controlled trial.Cost effectiveness and resource allocation : C/E · 2026Article
- Pursuing Reduction in Fatigue After COVID-19 via Exercise and Rehabilitation (PREFACER): a protocol for a randomised feasibility trial.BMJ open · 2025Article
- Review
- Effectiveness of Japanese traditional medicine (Kamikihito and Saikokeishito) for treating long COVID: a prospective observational study.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Long COVID continues to impose a significant burden on COVID-19 survivors, presenting with diverse symptoms and clinical uncertainty. This review synthesized evidence from 97 studies, including 26 randomized controlled trials and 15 non-randomized comparative studies, which explored the effectiveness, comparative effectiveness, and potential risks of proposed interventions for managing common long COVID symptoms: fatigue, neurocognitive symptoms, anxiety, depression, and sleep issues. Our comprehensive analysis, encompassing English-language articles, gray literature, and feedback from 14 Key Informants (i.e., patients, caregivers, clinicians, payors, and researchers), reveals a persistently weak body of evidence, characterized by high imprecision and considerable uncertainty regarding the benefits and harms of the interventions. The studies examined a wide array of treatment categories, including multi-component rehabilitation, supplements, complementary treatments, prescription medications, and the COVID-19 vaccine. Key informants emphasized the critical need for establishing robust diagnostic criteria and utilizing functional outcomes while also highlighting significant barriers to care, including dismissive attitudes from healthcare providers, inadequate insurance coverage, and restricted access to specialty care. Given the evolving definitions of long COVID and the variable mechanisms of its management, our findings underscore the pressing need for further rigorous research to refine and validate effective treatment protocols. Until more definitive evidence is available, both clinicians and patients face substantial uncertainty in treatment decisions, with many resorting to self-treatment using costly and potentially ineffective options.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.