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Thromboembolic Complications in Severe COVID-19: Current Antithrombotic Strategies and Future Perspectives

2021· review· en· W3136028292 on OpenAlexaff
Juan José Berenguer Rodríguez, Oscar C. Muñoz, Mateo Porres‐Aguilar, Debabrata Mukherjee

Bibliographic record

VenueCardiovascular & Haematological Disorders - Drug Targets · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsAntithromboticMedicineCoronavirus disease 2019 (COVID-19)PandemicIntensive care medicineEpidemiologyDiseaseCoronavirusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakClinical trialInternal medicineOutbreakInfectious disease (medical specialty)Virology

Abstract

fetched live from OpenAlex

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus (SARS- CoV-2) is our latest pandemic and has turned out to be a global public health crisis. One of the special characteristics of this disease is that it may predispose patients to thrombotic disease both in the venous and arterial circulation. We review arterial and venous thromboembolic complications in patients with COVID-19, epidemiology, pathogenesis, hematologic biomarkers, and current antithrombotic strategies. Future perspectives and clinical trials are ongoing to determine the best thromboprophylaxis strategies in the hospitalized patients with severe COVID-19.

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)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.073
GPT teacher head0.428
Teacher spread0.355 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2021
Admission routes1
Has abstractyes

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