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Record W3188666308 · doi:10.2217/cer-2021-0150

Economic burden of rivaroxaban and warfarin among nonvalvular atrial fibrillation patients with obesity and polypharmacy

2021· article· en· W3188666308 on OpenAlexaff
François Laliberté, Veronica Ashton, Akshay Kharat, Dominique Lejeune, Kenneth Todd Moore, Young Yun Jung, Patrick Lefèbvre, Jeffrey S. Berger

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2021
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsGroup for Research in Decision Analysis
FundersJanssen Scientific Affairs
KeywordsMedicineRivaroxabanPolypharmacyAtrial fibrillationWarfarinObesityInternal medicineCardiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Aim: Evaluate healthcare resource utilization (HRU) and costs associated with rivaroxaban and warfarin among nonvalvular atrial fibrillation (NVAF) patients with obesity and polypharmacy. Materials & methods: IQVIA PharMetrics ® Plus (January 2010–September 2019) data were used to identify NVAF patients with obesity (BMI ≥30 kg/m 2 ) and polypharmacy (≥5 medications) initiated on rivaroxaban or warfarin. Weighted rate ratios and cost differences were evaluated post-treatment initiation. Results: Rivaroxaban was associated with significantly lower rates of HRU, including hospitalization (rate ratio [95% CI]: 0.83 [0.77, 0.92]). Medical costs were reduced in rivaroxaban users (difference [95% CI]: -US$6868 [-US$10,628, -US$2954]), resulting in significantly lower total healthcare costs compared with warfarin users (difference [95% CI]: -US$4433 [-US$8136, -US$582]). Conclusion: Rivaroxaban was associated with lower HRU and costs compared with warfarin among NVAF patients with obesity and polypharmacy in commercially insured US patients.

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)0.001
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 score0.007
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.087
GPT teacher head0.422
Teacher spread0.335 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

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