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When can we stop anticoagulation in patients with cancer-associated thrombosis?

2017· review· en· W2771553990 on OpenAlexaff
Agnes Lee

Bibliographic record

VenueHematology · 2017
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersLEO PharmaPfizerBristol-Myers Squibb
KeywordsMedicineIntensive care medicineAnticoagulantAnticoagulant therapyCancerThrombosisCase fatality rateVenous thromboembolismRisk assessmentEpidemiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

The optimal duration of anticoagulant therapy in patients with cancer-associated venous thromboembolism (VTE) is unknown. Without well-designed studies evaluating the efficacy, safety, and cost-effectiveness of continuing anticoagulant therapy beyond the acute treatment period of 3 to 6 months, evidence-based recommendations are lacking. Consensus guidelines generally suggest continuing anticoagulation treatment in patients with active cancer or receiving cancer treatment, with periodic reassessment of the risks and benefits. Unfortunately, with very little published data on the epidemiology of cancer-associated VTE beyond the initial 6 months, it is not possible for clinicians and patients to weigh risks and benefits in a quantitatively informed manner. Further research is needed to provide reliable and contemporary estimates on the risk of recurrent VTE off anticoagulant therapy, risk of bleeding on anticoagulant therapy, case fatality or all-cause mortality, and other important consequences of living with cancer-associated VTE. This chapter provides an overview of the published literature on real-world data on anticoagulant therapy use, the risks and risk factors of recurrent VTE and bleeding, and patient preference and values regarding long-term anticoagulation treatment. It will conclude with a pragmatic, experience-informed approach for tailoring anticoagulant therapy in patients with cancer-associated VTE.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
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 head0.084
GPT teacher head0.373
Teacher spread0.288 · 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 designNot applicable
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

Citations16
Published2017
Admission routes1
Has abstractyes

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