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Record W4283315402 · doi:10.4081/monaldi.2022.2276

Cardiac biomarkers and mortality in COVID-19 infection: A review

2022· review· en· W4283315402 on OpenAlex
Angelica Cersosimo, Giuliana Cimino, Ludovica Amore, Emiliano Calvi, Greta Pascariello, Riccardo M. Inciardi, Carlo Lombardi, Enrico Vizzardi, Marco Metra

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMonaldi Archives for Chest Disease · 2022
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineInternal medicineNatriuretic peptideDiseaseTroponinCoronavirus disease 2019 (COVID-19)CardiologyBiomarkerCreatine kinaseMortality rateIntensive care medicineMyocardial infarctionHeart failureInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Lots of meta-analysis emphasize that a great number of hospitalized patients with moderate and severe forms of COVID-19 developed acute myocardial damage, defined as an increase of cardiac biomarkers, such N-terminal pro-B-type natriuretic peptide (NT-pro-BNP), creatine kinase-myocardial band (CK-MB) and of all type of troponins. The highest mortality rate is related with progressively increasing biomarkers levels and with a history of cardiovascular disease. In fact, the biomarkers dosage should be considered as a prognostic marker in all patients with COVID-19 disease at admission, during hospitalization and in the case of clinical deterioration. The purpose of this review is to evaluate cardiovascular prognostic factors in COVID-19 disease throughout the analysis of cardiac biomarkers to early identify the most serious patients and to optimize their outcomes.

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.

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)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.153
GPT teacher head0.491
Teacher spread0.338 · 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