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Record W2004250367 · doi:10.1002/jcph.435

The use of antidepressants and the risk of chronic atrial fibrillation

2014· article· en· W2004250367 on OpenAlexafffund
Franco Lapi, Laurent Azoulay, Abbas Kezouh, Jacques I. Benisty, Ilan Matok, Alessandro Mugelli, Samy Suissa

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2014
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill University Health CentreMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineCohortDepression (economics)Internal medicineAtrial fibrillationConfidence intervalLogistic regressionIncidence (geometry)Cohort studyAnxietyRelative riskPsychiatry

Abstract

fetched live from OpenAlex

Serotonin stimulation of the 5HT4 receptor might be responsible for an increased risk of atrial fibrillation (AF). Thus, we assessed whether the use of antidepressants (ADs) is associated with an increased risk of chronic AF (cAF). Using the UK Clinical Practice Research Datalink, a nested case-control analysis was conducted within a cohort of new AD users having a diagnosis of depression and/or anxiety. Cases of cAF occurring during follow-up were individually matched with up to 10 controls on age, sex, year of cohort entry, and duration of follow-up. Conditional logistic regression was used to estimate rate ratios (RRs) and 95% confidence intervals (CIs) of cAF associated with current and recent use of ADs, when compared to past use. The cohort included 116,125 new AD users, of whom 1,271 were diagnosed with cAF during follow-up (incidence rate: 1.6 per 1,000 person-years). The adjusted RR of cAF associated with current and recent use of ADs was 0.98 (95%CI: 0.86-1.12) and 1.02 (95%CI: 0.86-1.30), respectively. No association was observed when ADs were classified according to their potency in reducing serotonin reuptake. These findings suggest that exposure to ADs is not associated with an increased risk of cAF.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.151
GPT teacher head0.454
Teacher spread0.303 · 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

Citations18
Published2014
Admission routes2
Has abstractyes

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