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Record W4280553986 · doi:10.1080/00015385.2022.2066778

Assessment of the QT interval in right bundle branch block

2022· article· en· W4280553986 on OpenAlexaff
Simon W. Rabkin

Bibliographic record

VenueActa cardiologica. Supplementum · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQT intervalMedicineRight bundle branch blockCardiologyHeart rateInternal medicineElectrocardiographyInterval (graph theory)AnesthesiaMathematicsBlood pressureCombinatorics

Abstract

fetched live from OpenAlex

Background Identifying prolonged QT interval in RBBB has been problematic.Methods Four approaches were compared to adjust for the QT prolongation in intermittent RBBB. The implications were assessed in a separate group of 200 persons with established RBBB.Results In 12 individuals, mean age 74.5 years with intermittent RBBB, the presence of RBBB significantly (p < 0.05) increased the QT interval in each of six different heart rate correction formulae by an amount ranging from 35.4 ms in the Hodges formula to 50.2 ms in the Bazett formula. Four different equations were tested to adjust the QT interval and one approach QTcRBBB = 0.945*QTcRBBB − 26 was the best method to adjust for the increased QT in RBBB as it produced a QT value that was not significantly different from the QT interval in the absence of RBBB in intermittent RBBB.Failure to adjust the QT interval in RBBB produces an overestimate of the QT interval which in some heart rate adjustment formulae was marked. For the Bazett heart rate adjustment approach QTc 450 ms was found in 73.9% of men and QTc over 460 ms was found in 60.6% of women.Conclusion These data suggest the implementation of a new approach to recalculate the QT intervals in RBBB. QTcRBBB = 0.945*QTcRBBB − 26 with an appropriate heart rate adjustment formula (other than the Bazett formula) accurately predicts the QT interval in the absence of RBBB.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.999

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.272
Teacher spread0.263 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueActa cardiologica. SupplementumSame topicCardiac electrophysiology and arrhythmiasFrench-language works237,207