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Record W3049583880 · doi:10.1503/cmaj.201240

Anticipating and managing coagulopathy and thrombotic manifestations of severe COVID-19

2020· review· en· W3049583880 on OpenAlexafffundvenue
Lucas C. Godoy, Ewan C. Goligher, Patrick R. Lawler, Arthur S. Slutsky, Ryan Zarychanski

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsResearch Institute in Oncology and HematologyToronto General HospitalUniversity Health NetworkUniversity of TorontoUniversity of ManitobaGolder Associates (Canada)St. Michael's Hospital
FundersCanadian Institutes of Health ResearchResearch Manitoba
KeywordsCoagulopathyCoronavirus disease 2019 (COVID-19)MedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCoronavirusIntensive care medicineDiseaseSeverity of illnessBetacoronavirusCoronavirus InfectionsPandemicVirologyInternal medicineInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

oronavirus disease 2019 (COVID-19), the disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a broad spectrum of manifestations and severity. 3] Macrovascular and microvascular thrombosis may contribute to organ failure, multisystem injury and death. 6,7 However, the optimal prevention and treatment of thrombosis in COVID-19 remains uncertain. We review emerging evidence regarding COVID-19 coagulopathy (Box 1) and discuss current therapeutic directions and unanswered questions.

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)0.003
metaresearch head score (Gemma)0.119
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.119
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.081
GPT teacher head0.447
Teacher spread0.367 · 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

Citations50
Published2020
Admission routes3
Has abstractyes

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