Abstract 109: Regional Cerebral Hypoperfusion In Patients Recovered From Mild COVID-19
Bibliographic record
Abstract
Background: Cerebral hypoperfusion have been described in both severe and mild forms of symptomatic coronavirus disease 2019 (COVID-19) infection. The purpose of this study was to investigate global and regional gray matter (GM) and white matter (WM) cerebral blood flow (CBF) in asymptomatic COVID-19 infection patients compared with age-gender-race matched controls. Methods: Cases with mild COVID-19 infection and age-gender-race matched healthy controls, were drawn from the ABC@UofSC data repository. Demographics, risk factors and data from the Montreal Cognitive Assessment (MOCA score) were collected within a week of magnetic resonance imaging (MRI) perfusion image acquisition using pseudo-continuous arterial spin labeling. Mean CBF values for GM, WM and whole brain were calculated by averaging CBF values of standard space normalized CBF image values falling within GM and WM masks. Whole-brain, region of interest (ROI) based analyses were used to create standardized cerebral blood flow maps and further explore differences between the two groups. Results: Twenty-eight cases with prior mild COVID-19 infection were compared with 28 age-, gender-, race-matched controls. The MOCA score was similar between cases and controls. Whole-brain CBF (46.7±5.6 vs. 49.3±3.7, p=0.05), GM-specific CBF (64.2±8.9 vs. 67.6±6.0, p=0.10), and WM-specific CBF (29.3±2.6 vs. 31.0±1.6, p=0.03) were noted to be lower in COVID-19 cases as compared to controls. Further analysis identified several brain regions with lower CBF than the CONTROL group colors representing Z-scores shown in the figure. Predictive models based on these data predicted COVID-19 group membership with a high degree of accuracy (85.2%) suggesting CBF patterns as a key imaging marker of mild infection. Conclusion: In this study, lower white matter CBF, as well as widespread regional CBF changes identified using quantitative MRI, were found in patients recovered from mild to moderate COVID-19 infection.
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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) | 0.002 | 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".