Brain fog with long covid and chemotherapy: systematic review and meta-analysis
Bibliographic record
Abstract
Question What are the cognitive, functional and affective characteristics of brain fog in individuals with long covid and following chemotherapy, and how are these features assessed across studies? Study selection and analysis In March 2024, we conducted a systematic review and meta-analysis of peer-reviewed studies assessing cognition, function or mood in adults (≥18 years) with brain fog after COVID-19 or chemotherapy. PubMed, Embase and Web of Science were searched systematically according to eligibility criteria to March 2024, with an update in May 2025. Random-effects meta-analyses using the ‘dmetar’ package (V.0.0.9000) in R V.4.3.1 were performed for studies comparing individuals with and without brain fog. Bias was assessed using the National Institutes of Health Study Quality Assessment Tools. Findings Of 3077 records screened, 65 studies met inclusion criteria: 40 investigated brain fog in long covid and 25 in chemotherapy populations. Considerable variation in assessment tools was observed. Montreal Cognitive Assessment was the most common cognitive test in long covid studies; Functional Assessment of Cancer Therapy—Cognitive Function was most used in chemotherapy studies. Nine long covid studies were eligible for meta-analysis. Compared with controls, individuals with brain fog had significantly lower cognitive performance (Hedge’s g=−0.63, 95% CI −1.15 to −0.12), higher fatigue (Hedge’s g=2.64, 95% CI 0.41 to 4.86) and more depressive symptoms (Hedge’s g=1.48, 95% CI 0.40 to 2.55). Heterogeneity was high (I 2 >70%). No chemotherapy studies were appropriate for meta-analysis, preventing direct comparison of brain fog features between long covid and chemotherapy groups. Conclusions Brain fog in long covid and chemotherapy populations is associated with cognitive complaints, fatigue and mood disturbance, though assessment methods differ widely. To improve comparability and clinical understanding, we propose adoption of consistent tools and definitions in future studies. This will be a crucial step in generating findings that can be meaningfully compared across populations. PROSPERO registration number CRD42024520549.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".