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Cardiac Magnetic Resonance and Cardiac Magnetic Field Mapping in a Patient with Stress‐Induced Cardiomyopathy (Tako‐Tsubo)

2006· article· en· W2016378889 on OpenAlexaff
Robert Fischer, Alexander Schirdewan, Andreas Kumar, A. Gapelyuk, Jeanette Schulz‐Menger, Rainer Dietz

Bibliographic record

VenuePacing and Clinical Electrophysiology · 2006
Typearticle
Languageen
FieldMedicine
TopicTakotsubo Cardiomyopathy and Associated Phenomena
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineCardiologyCardiomyopathyInternal medicineCardiac magnetic resonanceMagnetic resonance imagingMyocardial fibrosisPathologicalCardiac magnetic resonance imagingRepolarizationSudden cardiac deathElectrocardiographyFibrosisHeart failureRadiologyElectrophysiology

Abstract

fetched live from OpenAlex

We encountered a 65-year-old woman with typical electrocardiogram (ECG) changes and new-onset left ventricular dysfunction with apical ballooning that exhibited typical changes of tako-tsubo-like cardiomyopathy. We used cardiac magnetic resonance (CMR) and cardiac magnetic field mapping (CMFM) to detect changes in structural, mechanical, and electrophysiological myocardial properties during follow-up. CMR displayed an acute myocardial injury, but neither fibrosis nor necrosis. CMFM exhibited severely disturbed repolarization with an inhomogeneous magnetic field. These pathological findings persisted much longer than the abnormalities detected by CMR and the ECG.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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 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

Citations4
Published2006
Admission routes1
Has abstractyes

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