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Record W4402381029 · doi:10.1080/13506129.2024.2398446

International prevalence of transthyretin amyloid cardiomyopathy in high-risk patients with heart failure and preserved or mildly reduced ejection fraction

2024· article· en· W4402381029 on OpenAlexfundno aff
Sergi Yun, Giovanni Palladini, Eve Cariou, Ronnie Wang, Franca S. Angeli, Ben Ebede, Pablo García‐Pavía

Bibliographic record

VenueAmyloid · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsnot available
FundersUniwersytet ŁódzkiKeio UniversityProthenaNovo NordiskUniversity of OttawaAstraZenecaAlnylam PharmaceuticalsAlexion PharmaceuticalsPfizerBristol-Myers Squibb
KeywordsTransthyretinEjection fractionHeart failureMedicineCardiomyopathyInternal medicineCardiologyHeart failure with preserved ejection fractionAmyloid (mycology)AmyloidosisPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an underdiagnosed cause of heart failure (HF). METHODS: This epidemiology study assessed the international prevalence of ATTR-CM among patients aged ≥60 years with a history of HF, left ventricular ejection fraction (LVEF) >40%, an end-diastolic interventricular septum thickness (IVST) ≥12 mm, but without diagnosed amyloidosis, history of LVEF ≤40%, cardiomyopathy of known cause, severe valvular, or coronary heart disease. ATTR-CM was determined using cardiac scintigraphy alongside exclusionary testing for light chain amyloidosis. The study was terminated early due to slow recruitment, without safety concerns. RESULTS: <.05) included a history of carpal tunnel syndrome, higher N-terminal pro B-type natriuretic peptide concentration, and higher end-diastolic IVST. CONCLUSIONS: ATTR-CM was diagnosed in 18% (95% CI: 13.7-22.5) of evaluable patients with HF, LVEF >40%, and risk markers for ATTR-CM, but no previous diagnosis of amyloidosis. Recruitment bias may have contributed to regional variability. NCT04424914.

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 score0.075
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.236
Teacher spread0.231 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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