Quantitative brain volume differences between COVID-19 patients and non-COVID-19 volunteers: A systematic review
Bibliographic record
Abstract
BACKGROUND: The majority of COVID-19 neuroimaging literature focuses on the acute period after infection and clinically evident lesions. The chronic effects of COVID-19 on brain structure are less well defined. There are inconsistencies in the existing structural neuroimaging studies regarding differences in brain volumes after COVID-19 infection. It was thus our aim to systematically evaluate the structural neuroimaging literature focusing on volumetric differences between patients with COVID-19, and volunteers without COVID-19, at greater than 6 weeks post-infection. METHODS: PubMed, Embase, and Scopus were searched in May 2023 with an updated search in September 2024, for studies with a quantitative comparison of brain volumes between COVID-19 patients and non-COVID-19 volunteers with MRI acquired more than 6-weeks after COVID-19. Exclusion criteria included COVID-19 patients selected for the presence of specific symptoms, case reports and case studies, and post-mortem studies. Forwards and backwards citation chasing were performed. RESULTS: Sixteen studies met inclusion criteria. The majority of studies reported smaller grey matter volumes amongst COVID-19 patients compared to healthy volunteers. However, there were inconsistent findings, with 3 studies reporting larger grey matter volumes in the COVID-19 groups. Additionally, studies with COVID-19 cohorts with more severe presentations, characterized by admission to the hospital or the ICU, were more likely to report smaller grey matter volumes compared to healthy volunteers, than studies that were focused on patients who recovered at home. CONCLUSION: A systematic review of the literature indicates that COVID-19 illness severity may explain some of the heterogeneity in brain volume differences between COVID-19 patients and healthy volunteers. More longitudinal follow-up studies are needed to assess the longitudinal course of COVID-19's effects on brain volumes.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".