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Record W2010386763 · doi:10.1182/blood-2012-06-415943

How I treat anticoagulated patients undergoing an elective procedure or surgery

2012· review· en· W2010386763 on OpenAlex
Alex C. Spyropoulos, James D. Douketis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBlood · 2012
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineApixabanRivaroxabanDabigatranWarfarinAnticoagulantIntensive care medicineBleedHeparinFondaparinuxSurgeryThrombosisAtrial fibrillationVenous thromboembolismInternal medicine

Abstract

fetched live from OpenAlex

The periprocedural management of patients receiving long-term oral anticoagulant therapy remains a common but difficult clinical problem, with a lack of high-quality evidence to inform best practices. It is a patient's thromboembolic risk that drives the need for an aggressive periprocedural strategy, including the use of heparin bridging therapy, to minimize time off anticoagulant therapy, while the procedural bleed risk determines how and when postprocedural anticoagulant therapy should be resumed. Warfarin should be continued in patients undergoing selected minor procedures, whereas in major procedures that necessitate warfarin interruption, heparin bridging therapy should be considered in patients at high thromboembolic risk and in a minority of patients at moderate risk. Periprocedural data with the novel oral anticoagulants, such as dabigatran, rivaroxaban, and apixaban, are emerging, but their relatively short half-life, rapid onset of action, and predictable pharmacokinetics should simplify periprocedural use. This review aims to provide a practical, clinician-focused approach to periprocedural anticoagulant management.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.163
GPT teacher head0.362
Teacher spread0.199 · 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