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Record W2019140346 · doi:10.1586/erp.12.79

Stop the clots, but at what cost? Pharmacoeconomics of dabigatran etexilate for the prevention of stroke in subjects with atrial fibrillation: a systematic literature review

2013· review· en· W2019140346 on OpenAlexaboutno aff
Sarah J. Marshall, Patricia Fearon, Jesse Dawson, Terence J. Quinn

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2013
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsDabigatranMedicinePharmacoeconomicsAtrial fibrillationStroke (engine)WarfarinIntensive care medicineRandomized controlled trialRivaroxabanTolerabilityClinical trialIntracerebral hemorrhageAdverse effectInternal medicine

Abstract

fetched live from OpenAlex

Dabigatran etexilate is a newly approved agent for prophylaxis of stroke in atrial fibrillation. Through narrative review, the authors assess evidence of the efficacy of dabigatran in stroke prevention, focusing on the multicenter, randomized trial RE-LY. The authors complement this with a review of the clinical efficacy of standard treatments (antiplatelet and warfarin). Finally, the authors present a systematic review of published studies describing the economics of dabigatran. Our systematic search gave six economic reviews from a variety of healthcare systems (the USA, Canada and the UK) and utilizing different economic models. Analyses suggest economic benefit of high- or sequential-dose dabigatran, particularly when stroke risk is high; intracerebral hemorrhage risk is high or warfarin control is poor. However, questions remain around dabigatran tolerability, compliance and possible unexpected adverse events.

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0060.006
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.119
GPT teacher head0.530
Teacher spread0.410 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2013
Admission routes1
Has abstractyes

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