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Record W4229449191 · doi:10.1016/j.radcr.2022.04.013

MRI findings of epipericardial fat necrosis: As a rare cause of acute chest pain in a healthy man

2022· article· en· W4229449191 on OpenAlexaff
Moezedin Javad Rafiee, Mahya Khaki, Leila Haririsanati, Faranak Babaki Fard, Michael Chetrit, Matthias G. Friedrich

Bibliographic record

VenueRadiology Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicOmental and Epiploic Conditions
Canadian institutionsUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsMedicineChest painPericardiumMagnetic resonance imagingAcute coronary syndromeRadiologyEmergency departmentLesionCardiac magnetic resonance imagingNecrosisMyocardial infarctionCardiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Epipericardial fat necrosis (EPFN) is a rare, benign cause of acute chest pain imitating symptoms of life-threatening diseases, such as acute coronary syndrome. Here We report a 37-year-old, healthy male presented to the emergency department (ED) with sudden-onset pleuritic chest pain after an isometric physical training. Initial cardiac workup included ECG, echocardiography was unremarkable, but diagnosis of an inflammatory process that involved the epipericardial fat tissue surrounding the heart was made by showing encapsulated fatty lesion, enhanced adjacent parietal pericardium using of contrast-enhanced magnetic resonance imaging (MRI). Magnetic resonance imaging would help physicians to differentiate EPFN from severe and life-treating conditions.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
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 head0.015
GPT teacher head0.291
Teacher spread0.275 · 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 designCase report
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
Published2022
Admission routes1
Has abstractyes

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