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Record W2140742930 · doi:10.1002/jcph.479

Proton pump inhibitor use is not associated with cardiac arrhythmia in critically ill patients

2015· article· en· W2140742930 on OpenAlexaff
Kenneth P. Chen, Joon Lee, Roger G. Mark, Mengling Feng, Leo Anthony Celi, Brian Malley, John Danziger

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2015
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsUniversity of Waterloo
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of General Medical Sciences
KeywordsMedicineCardiac arrhythmiaHypomagnesemiaInternal medicineCardiologyProton-pump inhibitorCritically illSudden cardiac deathCohortCohort studyHeart failureAtrial fibrillation

Abstract

fetched live from OpenAlex

Hypomagnesemia can lead to cardiac arrythmias. Recently, observational data have linked chronic proton pump inhibitor (PPI) exposure to hypomagnesemia. Whether PPI exposure increases the risk for arrhythmias has not been well studied. Using a large, single-center inception cohort of critically ill patients, we examined whether PPI exposure was associated with admission electrocardiogram readings of a cardiac arrhythmia in more than 8000 patients. There were 25.4% PPI users, whereas 6% were taking a histamine 2 antagonist. In all, 14.0% had a cardiac arrhythmia. PPI use was associated with an unadjusted risk of arrhythmia of 1.15 (95% CI,1.00-1.32; P =.04) and an adjusted risk of arrhythmia of 0.91 (95% CI, 0.77-1.06; P =.22). Among diuretic users (n = 2476), PPI use was similarly not associated with an increased risk of cardiac arrhythmia. In summary, in a large cohort of critically ill patients, PPI exposure is not associated with an increased risk of cardiac arrhythmia.

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)0.004
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.013
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 head0.097
GPT teacher head0.428
Teacher spread0.330 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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