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Record W2005837250 · doi:10.1053/euhj.2001.2986

Amiodarone reduces procedures and costs related to atrial fibrillation in a controlled clinical trial

2002· article· en· W2005837250 on OpenAlexafffundabout
Gerald Lumer

Bibliographic record

VenueEuropean Heart Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsComputer Research Institute of Montréal
FundersCanadian Institutes of Health Research
KeywordsMedicineAtrial fibrillationAmiodaroneRandomized controlled trialCardiologyInternal medicineClinical trialIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Atrial fibrillation is the most common sustained cardiac arrhythmia, and engenders significant health care costs. The impact of various treatment options for atrial fibrillation on hospital costs has not been evaluated in a randomized trial. METHODS: We analysed 1-year follow-up data on 392 patients randomized to low dose amiodarone (200 mg. day(-1)) or alternative first-line therapy (sotalol or propafenone) in a multicentre trial (Canadian Trial of Atrial Fibrillation, CTAF). RESULTS: Patients in the amiodarone group had fewer electrical cardioversions (65 vs 109 for patients in the sotalol/propafenone group, P<0.0001), and pacemaker insertions (4 vs 11, P=0.07). The average amiodarone patient spent fewer days in hospital (0.47 vs 0.97, P=0.01), and incurred lower costs ($532 vs $898, P=0.03), for admissions where atrial fibrillation was the admitting diagnosis. Average total hospital costs per patient for all admissions, as well as average combined hospital and physician costs per patient, showed wide variations within the treatment arms and were not significantly different between groups. CONCLUSION: For patients in whom antiarrhythmic drug therapy is indicated, low dose amiodarone significantly reduces atrial fibrillation-related costs by reducing the number of atrial fibrillation-related procedures.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.140
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.000
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 head0.139
GPT teacher head0.408
Teacher spread0.269 · 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

Citations33
Published2002
Admission routes3
Has abstractyes

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