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Record W2129428462 · doi:10.1177/1076029611412362

Prevention of Deep Vein Thrombosis and Pulmonary Embolism in Patients With Stroke

2011· review· en· W2129428462 on OpenAlexafffund
Thalia S. Field, Michael D. Hill

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2011
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
FundersAlberta Innovates - Health SolutionsNational Stroke FoundationHeart and Stroke Foundation of Canada
KeywordsMedicinePulmonary embolismStroke (engine)Compression stockingsDeep veinIntermittent pneumatic compressionThrombosisHeparinVenous thrombosisChemoprophylaxisAdverse effectIntensive care medicineSurgeryAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Venous thromboembolism (VTE), encompassing deep venous thrombosis and pulmonary embolism, is a potentially fatal but preventable complication of stroke. Reported rates of VTE after stroke have decreased over the last four decades, possibly due to the implementation of stroke units, early mobilization and hydration, and increased early use of antiplatelets. Additional means of thromboprophylaxis in stroke include mechanical methods (ie, compression stockings) to prevent venous stasis and medical therapy including antiplatelets, heparins, and heparinoids. Risk of VTE must be balanced by potential risk of hemorrhagic complications from pharmacotherapy. Unfractionated heparin, low-molecular-weight heparin (LMWH), and danaparoid are acceptable options for chemoprophylaxis though none have shown superior efficacy for VTE prevention without an associated increase in major hemorrhage. The efficacy and timing of pharmacological thromboprophylaxis in hemorrhagic stroke are not well defined. Graduated compression stockings are associated with an increased rate of adverse events and are not recommended and intermittent pneumatic compression stockings require further investigation.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.088
GPT teacher head0.366
Teacher spread0.278 · 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

Citations34
Published2011
Admission routes2
Has abstractyes

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