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Record W4393175743 · doi:10.1016/s2589-7500(24)00024-4

Artificial intelligence-driven cardiac amyloidosis screening

2024· letter· en· W4393175743 on OpenAlexaff
Jacob Abdaem, Robert J.H. Miller

Bibliographic record

VenueThe Lancet Digital Health · 2024
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsAmyloidosisCardiac amyloidosisMedicineComputer scienceIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Transthyretin (ATTR) cardiac amyloidosis is an increasingly recognised disease, especially among older patients with aortic stenosis and heart failure.1 Although the prognosis of patients with ATTR-cardiac amyloidosis is poor without treatment,1 early initiation of targeted therapy reduces all-cause mortality and cardiovascular-related hospitalisations.2 Bone scintigraphy imaging has emerged as a highly accurate method for identifying patients with ATTR-cardiac amyloidosis. However, early identification of patients with ATTR-cardiac amyloidosis remains a clinical challenge, with a median time from symptom onset to diagnosis of 2 years.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.054
GPT teacher head0.325
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2024
Admission routes1
Has abstractyes

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