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Record W4380680957 · doi:10.3389/fcvm.2023.1186556

Phenotyping myocardial injury related to COVID and SARS-CoV-2 vaccination: insights from cardiovascular magnetic resonance

2023· article· en· W4380680957 on OpenAlexaff
Ria Garg, Muzna Hussain, Matthias G. Friedrich

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medicine2019-20 coronavirus outbreakMagnetic resonance imagingCardiologyVaccinationVirologyInternal medicineDiseaseRadiologyOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Myocardial injury in acute COVID-19 PrevalenceThough the most virulent manifestation of COVID-19 is acute respiratory distress syndrome, cardiac injury reflected through elevated troponin concentrations has been increasingly reported (8,9).Various studies have reported an overall prevalence of acute myocardial injury ranging 5%-38% (10).One study described myocardial injury prevalence of 36% with significant association with death and a higher troponin-I associated with a higher risk of death (11). Clinical presentationMyocardial injury is typically characterized by chest pain, dyspnea and palpitations, with or without elevated cardiac biomarkers such as high-sensitivity cardiac troponin (hsTn) and/

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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 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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