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Record W2960721446 · doi:10.1016/j.jchf.2019.04.010

Screening for Transthyretin Amyloid Cardiomyopathy in Everyday Practice

2019· review· en· W2960721446 on OpenAlexaff
Ronald Witteles, Sabahat Bokhari, Thibaud Damy, Perry Elliott, Rodney H. Falk, Nowell M. Fine, Мариана Господинова, Laura Obici, Claudio Rapezzi, Pablo García‐Pavía

Bibliographic record

VenueJACC Heart Failure · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersNational Institute for Health and Care ResearchPfizer
KeywordsTransthyretinMedicineIntensive care medicineContext (archaeology)DiseaseHeart failureCardiomyopathyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a life-threatening, progressive, infiltrative disease caused by the deposition of transthyretin amyloid fibrils in the heart, and can often be overlooked as a common cause of heart failure. Delayed diagnosis due to lack of disease awareness and misdiagnosis results in a poorer prognosis. Early accurate diagnosis is therefore key to improving patient outcomes, particularly in the context of both the recent approval of tafamidis in some countries (including the United States) for the treatment of ATTR-CM, and of other promising therapies under development. With the availability of scintigraphy as an inexpensive, noninvasive diagnostic tool, the rationale to screen for ATTR-CM in high-risk populations of patients is increasingly warranted. Here the authors propose a framework of clinical scenarios in which screening for ATTR-CM is recommended, as well as diagnostic "red flags" that can assist in its diagnosis among the wider population of patients with heart failure.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.038
GPT teacher head0.350
Teacher spread0.311 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations345
Published2019
Admission routes1
Has abstractyes

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