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Record W2231905616 · doi:10.19144/1911-1606.10.3.7

Löffler’s Endocarditis: First Report of Successful Mitral and Tricuspid Valve Replacements in a Patient with Long-Standing Hypereosinophilia

2015· article· en· W2231905616 on OpenAlexaffvenue
Neil P. Naik, Sahar Iqbal

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicEosinophilic Disorders and Syndromes
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHypereosinophiliaMedicineCardiologyInternal medicineEndocarditisTricuspid valveEosinophilia

Abstract

fetched live from OpenAlex

Löffler’s endocarditis is a condition that occurs in patients with hypereosinophilic syndrome (HES). First described in 1975, HES is a relatively new syndrome that is characterized by elevated eosinophils with end organ damage. Hypereosinophilia is defined as elevated eosinophils of 1.5 x 109/L for at least one month, or tissue involvement as evidenced by bone marrow section with 20% eosinophils, tissue infiltration, and/or deposition of eosinophil granule proteins in tissue. Prevalence of HES in the United States is estimated to be 0.36–6.3/ 100,000 people, of which Löffler’s endocarditis represents only a fraction of cases. We present a case where a patient presented with palpitations and shortness of breath, with a background of chronically elevated eosinophils. HES was previously diagnosed in this patient due to persistently elevated eosinophils with lung damage (Löffler’s syndrome). On biopsy, she was found to have endomyocardial fibrosis with a thickened endocardium secondary to eosinophils. Echocardiogram showed moderate to severe regurgitation in both mitral and tricuspid valves due to regurgitation secondary to fibrosis. She subsequently underwent a successful dual valve replacement. This is the first case, to our knowledge, where a dual valve replacement was performed successfully in a patient with Löffler’s endocarditis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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.

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

Citations0
Published2015
Admission routes2
Has abstractyes

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