Intervention modalities for brain fog caused by long-COVID: systematic review of the literature
Bibliographic record
Abstract
Individuals suffering from long-COVID can present with "brain fog", which is characterized by a range of cognitive impairments, such as confusion, short-term memory loss, and difficulty concentrating. To date, several potential interventions for brain fog have been considered. Notably, no systematic review has comprehensively discussed the impact of each intervention type on brain fog symptoms. We included studies on adult (aged > 18 years) individuals with proven long- COVID brain-fog symptoms from PubMed, MEDLINE, Central, Scopus, and Embase. A search limit was set for articles published between 01/2020 and 31/12/2023. We excluded studies lacking an objective assessment of brain fog symptoms and patients with preexisting neurological diseases that affected cognition before COVID-19 infection. This review provided relevant information from 17 studies. The rehabilitation studies utilized diverse approaches, leading to a range of outcomes in terms of the effectiveness of the interventions. Six studies described noninvasive brain stimulation, and all showed improvement in cognitive ability. Three studies described hyperbaric oxygen therapy, all of which showed improvements in cognitive assessment tests and brain perfusion. Two studies showed that the use of Palmitoylethanolamide and Luteolin (PEA-LUT) improved cognitive impairment. Noninvasive brain stimulation and hyperbaric oxygen therapy showed promising results in the treatment of brain fog symptoms caused by long-COVID, with improved perfusion and cortical excitability. Furthermore, both rehabilitation strategies and PEA-LUT administration have been associated with improvements in symptoms of brain fog. Future studies should explore combinations of interventions and include longer follow-up periods to assess the long-term effects of these treatments.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".