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Record W4294368573 · doi:10.17615/tmws-6n15

Understanding COVID-19-associated coagulopathy

2022· article· en· W4294368573 on OpenAlexfundno aff
J.H. Morrissey, Emily Conway, M.T. Rondina, J.H. Griffin, N. Mackman, Frank L. van de Veerdonk, Lisa E. Gralinski, R.A. Campbell, A.S. Wolberg, D. Nugent, L.O. Mosnier, R.Q. Warren, Kyung Young Moon, K.M. Hoffmeister

Bibliographic record

VenueUNC Libraries · 2022
Typearticle
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchDepartment of Internal Medicine, University of UtahNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCoronavirus disease 2019 (COVID-19)Coagulopathy2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyPandemicMedicineInternal medicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

COVID-19-associated coagulopathy (CAC) is a life-threatening complication of SARS-CoV-2 infection. However, the underlying cellular and molecular mechanisms driving this condition are unclear. Evidence supports the concept that CAC involves complex interactions between the innate immune response, the coagulation and fibrinolytic pathways, and the vascular endothelium, resulting in a procoagulant condition. Understanding of the pathogenesis of this condition at the genomic, molecular and cellular levels is needed in order to mitigate thrombosis formation in at-risk patients. In this Perspective, we categorize our current understanding of CAC into three main pathological mechanisms: first, vascular endothelial cell dysfunction; second, a hyper-inflammatory immune response; and last, hypercoagulability. Furthermore, we pose key questions and identify research gaps that need to be addressed to better understand CAC, facilitate improved diagnostics and aid in therapeutic development. Finally, we consider the suitability of different animal models to study CAC.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.606
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.170
GPT teacher head0.291
Teacher spread0.121 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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