MétaCan
Menu
Back to cohort

Cost-effectiveness of Apixaban Compared With Edoxaban for Stroke Prevention in Nonvalvular Atrial Fibrillation

2015· article· en· W1852447234 on OpenAlexaff
Gregory Y.H. Lip, Tereza Lanitis, Thitima Kongnakorn, Hemant Phatak, Corina Chalkiadaki, Xianchen Liu, Andreas Kuznik, Jack Lawrence, Paul Dorian

Bibliographic record

VenueClinical Therapeutics · 2015
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersPfizer
KeywordsMedicineEdoxabanApixabanAtrial fibrillationStroke (engine)Internal medicineCardiologyDabigatranRivaroxabanWarfarin

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this analysis was to assess the cost-effectiveness of apixaban 5 mg BID versus high- and low-dose edoxaban (60 mg and 30 mg once daily) as intended starting dose strategies for stroke prevention in patients from a UK National Health Service perspective. METHODS: A previously developed and validated Markov model was adapted to evaluate the lifetime clinical and economic impact of apixaban 5 mg BID versus edoxaban (high and low dose) in patients with nonvalvular atrial fibrillation. A pairwise indirect treatment comparison was conducted for clinical end points, and price parity was assumed between apixaban and edoxaban. Costs in 2012 British pounds, life-years, and quality-adjusted life-years (QALYs) gained, discounted at 3.5% per annum, were estimated. FINDINGS: Apixaban was predicted to increase life expectancy and QALYs versus low- and high-dose edoxaban. These gains were achieved at cost-savings versus low-dose edoxaban, thus being dominant and nominal increases in costs versus high-dose edoxaban. The incremental cost-effectiveness ratio of apixaban versus high-dose edoxaban was £6763 per QALY gained. IMPLICATIONS: Apixaban was deemed to be dominant (less costly and more effective) versus low-dose edoxaban and a cost-effective alternative to high-dose edoxaban.

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)0.000
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 score0.089
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.451
GPT teacher head0.504
Teacher spread0.053 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueClinical TherapeuticsSame topicAtrial Fibrillation Management and OutcomesFrench-language works237,207