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Record W4377087188 · doi:10.24875/cirue.m20000226

Thrombotic complications in severe COVID-19: focus on venous thromboembolism, thromboprophylaxis and anticoagulation

2023· article· en· W4377087188 on OpenAlexaff
Mateo Porres‐Aguilar, María Cristina Guerrero-de León, Flavio Adrián Grimaldo-Gómez, Raúl Izaguirre-Ávila, Alfredo Cabrera-Rayo, Luis E. Santos-Martínez, Jorge R. Sánchez-Medina, Mateo Porres‐Muñoz, Raúl Carrillo-Esper

Bibliographic record

VenueCirugía y Cirujanos (English Edition) · 2023
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsVenous thromboembolismMedicineCoronavirus disease 2019 (COVID-19)Venous thrombosisThrombosisIntensive care medicine2019-20 coronavirus outbreakSurgeryInternal medicineVirology

Abstract

fetched live from OpenAlex

Severe coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and predisposes to thrombotic complications.In this review, we address the close relationship between venous thromboembolism and COVID-19, providing practical aspects on epidemiology, risk factors, and thromboprophylaxis, as well as potential options of anticoagulation.The scientific data on COVID-19 is scarce; however, day by day we continue to learn, being aware of the evolving and dynamic changes in this emerging infectious and immunothrombotic disease.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.297
Teacher spread0.267 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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