<scp>MRI</scp> Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self‐Isolated Due to <scp>COVID</scp>‐19
Bibliographic record
Abstract
BACKGROUND: Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe. PURPOSE: To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19. STUDY TYPE: Prospective observational. POPULATION: A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis). FIELD STRENGTH AND SEQUENCES: A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences. ASSESSMENT: Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group. STATISTICAL TESTS: Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05. RESULTS: Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue. DATA CONCLUSION: These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".