MétaCan
Menu
Back to cohort
Record W4206992372 · doi:10.29173/cjen146

The Impact of COVID-19 on the Cardiovascular System

2022· article· en· W4206992372 on OpenAlexaffvenue
Mohamed Toufic El Hussein, Aditi Sharma

Bibliographic record

VenueCanadian Journal of Emergency Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsRockyview General HospitalMount Royal University
Fundersnot available
KeywordsMedicineDamagesIntensive care medicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Severe acute respiratory syndromeCoronavirus disease 2019 (COVID-19)DiseaseCoronavirusEmergency departmentAngiotensin-converting enzyme 2Mechanical ventilationInfectious disease (medical specialty)Emergency medicineMedical emergencyInternal medicineNursing

Abstract

fetched live from OpenAlex

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an infectious disease where symptoms can be mild, requiring no treatment or severe, requiring hospital admission for hemodynamic support and mechanical ventilation. Given the affinity of SARS-CoV-2 to angiotensin-converting enzyme 2 (ACE2) receptors, the heart is a highly susceptible target to its associated damages. Knowledge about SARS-CoV-2 modes of transmission and their impact on the cardiovascular system is paramount for emergency department (ED) nurses to protect themselves and competently care for their patients. The authors of this manuscript aim to provide a clinical overview of the impact of SARS-CoV-2 on the cardiovascular system based on the latest scientific evidence. A profound understanding of SARS-CoV-2 and its related consequences has the potential to minimize its associated mortality and morbidity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.101
GPT teacher head0.434
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Emergency NursingSame topicCOVID-19 Clinical Research StudiesFrench-language works237,207