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Record W2039742182 · doi:10.1007/s10897-014-9776-6

Reduced Uptake of Family Screening in Genotype‐Negative Versus Genotype‐Positive Long QT Syndrome

2014· article· en· W2039742182 on OpenAlexaff
Mikael Hanninen, George J. Klein, Zachary Laksman, Susan Conacher, Allan C. Skanes, Raymond Yee, Lorne J. Gula, Peter Leong‐Sit, Jaimie Manlucu, Andrew D. Krahn

Bibliographic record

VenueJournal of Genetic Counseling · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of British ColumbiaWestern UniversityGrey Nuns Community Hospital
Fundersnot available
KeywordsProbandLong QT syndromeGenetic counselingMedicineGenotypeFamily historyGenetic testingInternal medicineGeneticsMutationQT intervalGeneBiology

Abstract

fetched live from OpenAlex

The acceptance and yield of family screening in genotype-negative long QT syndrome (LQTS) remains incompletely characterized. In this study of family screening for phenotype-definite Long QT Syndrome (LQTS, Schwartz score ≥3.5), probands at a regional Inherited Cardiac Arrhythmia clinic were reviewed. All LQTS patients were offered education by a qualified genetic counselor, along with materials for family screening including electronic and paper correspondence to provide to family members. Thirty-eight qualifying probands were identified and 20 of these had family members who participated in cascade screening. The acceptance of screening was found to be lower among families without a known pathogenic mutation (33 vs. 77 %, p = 0.02). A total of 52 relatives were screened; fewer relatives were screened per index case when the proband was genotype-negative (1.7 vs. 3.1, p = 0.02). The clinical yield of screening appeared to be similar irrespective of gene testing results (38 vs. 33 %, p = 0.69). Additional efforts to promote family screening among gene-negative long QT families may be warranted.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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