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Record W2463148460 · doi:10.1136/tsaco-2016-000020

A surgeon's guide to anticoagulant and antiplatelet medications part one: warfarin and new direct oral anticoagulant medications

2016· review· en· W2463148460 on OpenAlexaff
Paul B. McBeth, Jordan A. Weinberg, Babak Sarani, Louise Yeung, Addison K. May

Bibliographic record

VenueTrauma Surgery & Acute Care Open · 2016
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineWarfarinAnticoagulantPerioperativeIntensive care medicineAnticoagulant therapyOral anticoagulantAnesthesiaAtrial fibrillationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

An increasing number of potent antiplatelet and anticoagulant medications are being used for the long-term management of cardiac, cerebrovascular, and peripheral vascular conditions. Management of these medications in the perioperative and peri-injury settings can be challenging for surgeons, mandating an understanding of these agents and the risks and benefits of various management strategies. In this two-part review, agents commonly encounter by surgeons in the perioperative and peri-injury settings are discussed and management strategies for patients on long-term antiplatelet and anticoagulant therapy reviewed. In part I, we review warfarin and the new direct oral anticoagulants. In part II, we review antiplatelet agents and assessment of platelet function and the perioperative management of long-term anticoagulant and antiplatelet therapy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.187
GPT teacher head0.426
Teacher spread0.240 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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