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Record W2096028378 · doi:10.1200/jco.2009.22.3958

Anticoagulation in the Treatment of Established Venous Thromboembolism in Patients With Cancer

2009· review· en· W2096028378 on OpenAlexaff
Agnes Lee

Bibliographic record

VenueJournal of Clinical Oncology · 2009
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsMedicineThrombosisIntensive care medicineCancerLow molecular weight heparinHeparinComplicationAnticoagulantVenous thrombosisPopulationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Cancer-associated thrombosis is a frequent and costly complication in patients with cancer. Significant morbidity and mortality not only result from thrombotic events, but may also occur as a result of the therapeutic interventions. The established treatment for cancer-associated thrombosis is anticoagulant therapy. Of the few options available, low molecular weight heparin (LMWH) is the preferred agent because of its efficacy, safety, and convenience. Alternatives to LMWH have undesirable limitations and have demonstrated poorer efficacy and safety in the oncology population. Treatment of recurrent thrombosis, patients with concurrent bleeding issues, role of vena cava filter insertion, and duration of therapy are all areas in need of urgent research. Treatment of cancer-associated thrombosis remains a challenging task and is limited by the paucity of evidence-based data. Research is urgently needed to advance current practice and improve patient care.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.142
GPT teacher head0.484
Teacher spread0.342 · 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 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

Citations61
Published2009
Admission routes1
Has abstractyes

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