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Record W3198421837 · doi:10.2471/blt.20.278473

Global access to affordable direct oral anticoagulants

2021· article· en· W3198421837 on OpenAlexaff

Bibliographic record

VenueBulletin of the World Health Organization · 2021
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversityImpact
FundersNational Institute for Health and Care ResearchWorld Health Organization
KeywordsReimbursementDiseaseGovernment (linguistics)RivaroxabanEssential medicinesIndirect costsOral anticoagulantDisease burden

Abstract

fetched live from OpenAlex

. The aims of this paper are to summarize the benefits of direct oral anticoagulants for patients with cardiovascular disease and to discuss ways of increasing their usage internationally. Although the cost of direct oral anticoagulants has provoked debate, the affordability of introducing these drugs into clinical practice could be increased by: price negotiation; pooled procurement; competitive tendering; the use of patent pools; and expanded use of generics. In 2017, only 14 of 137 countries that had adopted national essential medicines lists included a direct oral anticoagulant on their lists. This number could increase rapidly if problems with availability and affordability can be tackled. Once the types of patient likely to benefit from direct oral anticoagulants have been clearly defined in clinical practice guidelines, coverage can be more accurately determined and associated costs can be better managed. Government action is required to ensure that direct oral anticoagulants are covered by national budgets because the absence of reimbursement remains an impediment to achieving universal coverage. Tackling cardiovascular disease with the aid of direct oral anticoagulants is an essential component of efforts to achieve the World Health Organization's target of reducing premature deaths due to noncommunicable disease by 25% by 2025.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.371
Teacher spread0.315 · 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.

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
Published2021
Admission routes1
Has abstractyes

Explore more

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