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Record W2761389140 · doi:10.1097/hco.0000000000000465

Diagnosis and clinical management of long-QT syndrome

2017· review· en· W2761389140 on OpenAlexaff
Christian Steinberg

Bibliographic record

VenueCurrent Opinion in Cardiology · 2017
Typereview
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineRisk stratificationLong QT syndromeGenetic testingIntensive care medicineBioinformaticsSudden cardiac deathSudden deathPhenotypeNeuroscienceInternal medicineQT intervalGeneGenetics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To give an overview over the substantial advances in the diagnosis and management of Long-QT syndrome since its first description 60 years ago. RECENT FINDINGS: LQT syndrome remains the most common inherited arrhythmia and is a leading cause for sudden unexplained death accounting for up to 20-25% of cases. Rapid progress of genetic technology over the past 2 decades has significantly improved our understanding of molecular and genetic mechanisms of LQT. Despite all those novel insights, phenotype assessment and appropriate risk stratification in LQT remains challenging - even for the expert. SUMMARY: This review outlines our current understanding and approach to the clinical diagnosis and management of LQT as well as recent insights into genotype-phenotype correlations. Genetic testing has evolved beyond a pure diagnostic tool and is in addition increasingly integrated as complementary prognostic marker. With regard to the management of LQT, there is now evidence that the protective effect of beta-blockers is rather substance-specific than a class effect. Novel approaches - in conjunction with standard beta-blockers - are emerging including gene-specific treatment for certain subtypes of LQT. A specialized inherited arrhythmia clinic is the preferred resource for the complex risk stratification and individualized management of individuals with LQT.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.218
GPT teacher head0.492
Teacher spread0.274 · 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 designOther design
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

Citations45
Published2017
Admission routes1
Has abstractyes

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