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Record W2087879951 · doi:10.1182/blood-2013-04-460162

Treatment of cancer-associated thrombosis

2013· article· en· W2087879951 on OpenAlexaff
Agnes Lee, Erica A. Peterson

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsMedicineGynecologyLow molecular weight heparinApixabanThrombosisCancerVitamin K antagonistSurgeryInternal medicineWarfarinRivaroxabanAtrial fibrillation

Abstract

fetched live from OpenAlex

Therapeutic options for the management of venous thromboembolism (VTE) in patients with cancer remain very limited. Although low-molecular-weight heparin monotherapy has been identified as a simple and efficacious regimen compared with an initial parenteral anticoagulant followed by long-term therapy with a vitamin K antagonist, many clinical questions remain unanswered. These include optimal duration of anticoagulant therapy, treatment of recurrent VTE, and the treatment of patients with concurrent bleeding or those with a high risk of bleeding. Treatment recommendations from consensus clinical guidelines are largely based on retrospective reports or extrapolated data from the noncancer population with VTE, as randomized controlled trials focused on cancer-associated thrombosis are sorely lacking. Furthermore, with improvements in imaging technology and extended survival duration of patients with cancer, we are encountering more unique challenges, such as the management of incidental VTE. Clinicians should be aware of the limitations of the novel oral anticoagulants and avoid the use of these agents because of the paucity of evidence in the treatment of cancer-associated thrombosis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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 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

Citations181
Published2013
Admission routes1
Has abstractyes

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