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Record W2313196622 · doi:10.1055/s-0035-1544160

Novel or Non–Vitamin K Antagonist Oral Anticoagulants and the Treatment of Cancer-Associated Thrombosis

2015· review· en· W2313196622 on OpenAlexaff
Agnes Lee, Cynthia Wu

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2015
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of British ColumbiaUniversity of AlbertaVancouver Coastal Health
FundersLEO PharmaPfizer
KeywordsMedicineVitamin K antagonistThrombosisCancerDrugVenous thrombosisIntensive care medicineVenous thromboembolismClinical trialInternal medicinePharmacologyWarfarinAtrial fibrillation

Abstract

fetched live from OpenAlex

Cancer-associated thrombosis remains a common and challenging clinical presentation. Despite advances in therapy using low-molecular-weight heparins, both venous thromboembolic recurrence and clinically relevant bleeding while on therapeutic anticoagulation occur at high rates. Multiple novel (or non-vitamin K antagonist) oral anticoagulants have recently been developed for the treatment and prevention of venous thromboembolism. There are many attractive features of these agents including convenience and simplicity of administration. Unfortunately, there are also several limitations such as dependency on gastrointestinal absorption, renal clearance, and some significant drug-drug interactions. The use of these newer oral agents in cancer patients is not recommended, as their safety and efficacy are not yet established and the complexity of these patients warrants further cancer-specific clinical trials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.122
GPT teacher head0.411
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2015
Admission routes1
Has abstractyes

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