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Record W1989061918 · doi:10.1097/hco.0000000000000143

Anticoagulation strategies for left ventricular assist devices

2015· article· en· W1989061918 on OpenAlexaff
Hadi Toeg, Marc Ruel, Haissam Haddad

Bibliographic record

VenueCurrent Opinion in Cardiology · 2015
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineCardiologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Pump thrombosis can be a devastating complication in patients with a left ventricular assist device (LVAD). Treatment options with intravenous anticoagulation can lead to further complications. The present review discusses the current antithrombotic and anticoagulation strategies following LVAD implantation and during suspected pump thrombosis. RECENT FINDINGS: Recently, a significant increase in pump thrombosis (HeartMate II) at 3 months after LVAD implantation starting in March 2011 has been observed. This observation is likely multifaceted; however, recent changes in perioperative anticoagulation, accepting lower target international normalized ratios and lack of heparin bridging may play a substantial role. The International Society for Heart and Lung Transplantation published guidelines surrounding LVAD anticoagulation and management options in the setting of pump thrombosis. SUMMARY: Recommendations for thromboprophylaxis in patients with LVADs are scarce. The International Society for Heart and Lung Transplantation has put together minimum criteria for perioperative anticoagulation; however, this is on the basis of poor level of evidence (observational studies and expert opinion). Ultimately, clinicians will need to individualize the intensity and timing of anticoagulation following LVAD implantation to ensure adequate thromboprophylaxis while simultaneously minimizing bleeding.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.100
GPT teacher head0.339
Teacher spread0.238 · 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 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

Citations16
Published2015
Admission routes1
Has abstractyes

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