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Record W3045639144 · doi:10.1055/a-1174-1290

Management of Anticoagulant Treatment and Anticoagulation-Related Complications in Nonagenarians

2020· review· en· W3045639144 on OpenAlexaff
Michela Giustozzi, Lana A. Castellucci, Geoffrey D. Barnes

Bibliographic record

VenueHämostaseologie · 2020
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsAnticoagulant therapyAnticoagulantIntensive care medicineMedicineInternal medicine

Abstract

fetched live from OpenAlex

Given the aging population, the burden of age-dependent diseases is growing. Despite this, elderly patients are often underrepresented in clinical trials and little data are available on current anticoagulant management and outcomes in this unique population, especially those aged 90 years or older. There is uncertainty, and a fear of "doing harm," that often leads to de-prescription of antithrombotic agents in nonagenarian patients. Decision-making concerning the use of anticoagulant treatment needs to balance the risk of thrombotic events against the risk of major bleeding, especially intracranial hemorrhage. In this perspective, the development of direct oral anticoagulants (DOACs), acting as direct and selective inhibitors of a specific step or enzyme of the coagulation cascade, has dramatically changed oral anticoagulant treatment. In fact, given the lower incidence of intracranial hemorrhage, the favorable overall efficacy and safety, and the lack of routine monitoring, DOACs are the currently recommended anticoagulant agents for the treatment of both atrial fibrillation and venous thromboembolism even in very elderly patients. However, given the limited data available on the management of anticoagulation in nonagenarians, a few unanswered questions remain. In this review, we focused on recent evidence for anticoagulant treatment in atrial fibrillation and venous thromboembolism along with management of anticoagulation-related bleeding in nonagenarians.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.105
GPT teacher head0.379
Teacher spread0.275 · 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 designOther design
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

Citations7
Published2020
Admission routes1
Has abstractyes

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