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Record W3088645035 · doi:10.3904/kjim.2020.336

MicroCLOTS pathophysiology in coronavirus disease 2019

2020· letter· en· W3088645035 on OpenAlexaff
Samuele Renzi, Giovanni Landoni, Alberto Zangrillo, Fabio Ciceri

Bibliographic record

VenueThe Korean Journal of Internal Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineARDSPneumoniaPathophysiologyCoronavirusIntensive care medicineCoronavirus disease 2019 (COVID-19)PandemicDiseaseSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Randomized controlled trialRegimenImmunologyLungInternal medicineInfectious disease (medical specialty)

Abstract

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Severe acute respiratory syndrome coronavirus 2 ( SARS-CoV-2) is the novel coronavirus responsible for the ongoing pandemic. It is known that SARS-CoV-2 infects the host through the cell surface receptor of angiotensin-converting enzyme 2 (ACE2), which is expressed in multiple organs, and in the arterial and venous endothelial cells. We have recently proposed the use of the term MicroCLOTS ( Microvascular COVID-19 lung vessels obstructive thromboinflammatory syndrome) to describe the unique type of ARDS seen in patients affected by SARS-COV-2. After a multidisciplinary assessment of more than 850 COVID-19 patients admitted to our Hospital with several bilateral pneumonia, we have collected evidences supporting a key role of vascular inflammation and microthrombosis in the pathophysiology of the multisystemic clinical manifestations that have been associated with COVID-19. There is now a general consensus on the recommendation of anticoagulation in patient with severe SARS-Cov2 infections, although the dose of the prophylaxis and even the choice between a prophylactic and a treatment regimen remains controversial. Randomized controlled trials are urgently needed to help clarifying the many therapeutic challenges associated with the management of SARS-Cov-2 patients.

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)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.067
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.007
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 head0.065
GPT teacher head0.418
Teacher spread0.353 · 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
GenreCommentary

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

Citations21
Published2020
Admission routes1
Has abstractyes

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