MétaCan
Menu
Back to cohort
Record W2763773531 · doi:10.4172/2368-0512.1000092

Long QT syndrome with torsade in a patient with atrial fibrillation taking antihistamines– Case report

2017· article· en· W2763773531 on OpenAlexvenueno aff
Mariana Oliveira Marasca, Almir Alamino Lacalle, Giovana Saliba de Paula, Bárbara Dias de Souza, Alexia Campos Baldi, Juan Carlos Yugar‐Toledo, Elizabeth do Espírito Santo Cestário

Bibliographic record

VenueCurrent research. Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLong QT syndromeAtrial fibrillationQT intervalBrugada syndromeTerfenadineVentricular fibrillationShort QT syndromeAnesthesiaInternal medicineIntensive care medicineCardiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To report the case of a patient with long QT syndrome acquired after treatment of atrial fibrillation associated with the regular use of antihistamines. METHODS: The data of this work were obtained by reviewing medical records, the diagnostic tests to which the patient was submitted, and a literature review. FINAL CONSIDERATIONS: The reported case and published studies bring to light a discussion on long QT syndrome acquired consequent to the association of antiarrhythmic and antihistaminic drugs. An apparently innocuous association, however, different mechanisms of action on cardiac ion channels overlap, predisposing patients to acquire long QT syndrome and its feared complications.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.057
GPT teacher head0.380
Teacher spread0.323 · 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 designCase report
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCurrent research. CardiologySame topicCardiac electrophysiology and arrhythmiasFrench-language works237,207