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Record W3120157654 · doi:10.1177/1076029620975489

Retrospective Review of Prescribing Patterns in Cancer-Associated Thrombosis: A Single Center Experience in Edmonton, Alberta, Canada

2021· review· en· W3120157654 on OpenAlexaffabout
Hannah Kaliel, Meghan Mior, Steven Quan, Sunita Ghosh, Cynthia Wu, Tammy J. Bungard

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2021
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsMedicineWarfarinLow molecular weight heparinThrombosisVenous thromboembolismCancerAnticoagulantHeparinInternal medicineRetrospective cohort studyIntensive care medicineSurgeryAtrial fibrillation

Abstract

fetched live from OpenAlex

Low molecular weight heparin (LMWH) is the standard of care for treating cancer-associated thrombosis (CAT), although new evidence for direct oral anticoagulants (DOACs) supports use in specific cancer populations. In this retrospective review at a specialty CAT clinic from 2016 to 2019, we report the use of anticoagulants (LMWH, DOACs, warfarin, anticoagulant class change) in the acute and chronic phases of CAT and compare use before/after publication of the Hokusai-VTE Cancer trial. Death, venous thromboembolism (VTE) recurrence and bleeding was also reported. Of the 221 included, median age was 69 years, with 57.5% having metastatic disease. In the acute phase, 80.1% were prescribed LMWH, 4.1% DOAC, and 14.5% had an anticoagulant class change (LMWH to DOAC; 78.1%). In the chronic phase, 35.8% were prescribed LMWH, 11.3% DOAC, and 42.9% had an anticoagulant class change (LMWH to DOAC; 90.1%). Use of DOACs in the acute and chronic phase prior to the Hokusai-VTE trial was 1.0% and 2.0%, respectively, and following publication was 6.8% and 19.6%. Death occurred for 22.6% patients, recurrent VTE in 7.2%, and bleeding in 5.0%. DOAC use is increasing with time; real-world data may help to guide optimization of the care of complex patients.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.691
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.132
GPT teacher head0.406
Teacher spread0.274 · 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 designSystematic review
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

Citations5
Published2021
Admission routes2
Has abstractyes

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