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Record W2904680572 · doi:10.1182/blood-2018-06-820746

Treatment of bleeding complications in patients on anticoagulant therapy

2018· review· en· W2904680572 on OpenAlexaff
Siavash Piran, Sam Schulman

Bibliographic record

VenueBlood · 2018
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversityThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsMedicineFondaparinuxAnticoagulantHemostasisProthrombin complex concentrateRivaroxabanIntensive care medicineWarfarinAnticoagulant therapyAntifibrinolyticDiscovery and development of direct thrombin inhibitorsVitamin kSurgeryHeparinThrombinThrombosisTranexamic acidVenous thromboembolismInternal medicineAtrial fibrillationBlood loss

Abstract

fetched live from OpenAlex

Anticoagulant therapy is often refrained from out of fear of hemorrhagic complications. The most frequent type of major bleeding is gastrointestinal, but intracranial hemorrhage has the worst prognosis. Management of these complications in patients on anticoagulants should follow the same routines as for nonanticoagulated patients, as described here with the previously mentioned bleeds as examples. In addition, for life-threatening or massive hemorrhages, reversal of the anticoagulant effect is also crucial. Adequate reversal requires information on which anticoagulant the patient has taken and when the last dose was ingested. Laboratory data can be of some help, but not for all anticoagulants in the emergency setting. This is reviewed here for the different types of anticoagulants: vitamin K antagonists, heparins, fondaparinux, thrombin inhibitors and factor Xa inhibitors. Specific antidotes for the latter are becoming available, but supportive care and nonspecific support for hemostasis with antifibrinolytic agents or prothrombin complex concentrates, which are widely available, should be kept in mind.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.209
GPT teacher head0.411
Teacher spread0.201 · 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 designNot applicable
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

Citations107
Published2018
Admission routes1
Has abstractyes

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