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Record W4319007297 · doi:10.1161/str.54.suppl_1.109

Abstract 109: Regional Cerebral Hypoperfusion In Patients Recovered From Mild COVID-19

2023· article· en· W4319007297 on OpenAlexaboutno aff
Souvik Sen, Roger Newman‐Norlund, Nicholas Riccardi, Sarah Newman‐Norlund, Sara Sayers, Sarah Wilson, Natalie Busby, Samaneh Nemati, Chris Rorden, Julius Fridriksson

Bibliographic record

VenueStroke · 2023
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCerebral blood flowAsymptomaticWhite matterMagnetic resonance imagingInternal medicineCardiologyPerfusionPerfusion scanningCerebral perfusion pressureCoronavirus disease 2019 (COVID-19)DiseaseRadiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.036
GPT teacher head0.328
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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