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Record W2102201050 · doi:10.1517/14656566.4.12.2213

Anti-thrombotic therapy in cancer patients

2003· review· en· W2102201050 on OpenAlexafffund
Agnes Lee

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2003
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityMcGill University
FundersCanadian Institutes of Health Research
KeywordsMedicineIntensive care medicineCancerLow molecular weight heparinThrombosisHeparinVenous thromboembolismClinical trialAnticoagulantSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Primary and secondary prevention of venous thromboembolism (VTE) are the major roles of anti-thrombotic therapy in patients with cancer. Although the choices of pharmacological agents remain limited, low molecular weight heparins (LMWHs) offer important advantages over traditional anticoagulants. For prophylaxis in the surgical setting, once-daily subcutaneous injections of LMWH are as effective and safe as multiple doses of unfractionated heparin and extending prophylaxis with LMWH beyond hospitalisation can safely reduce the risk of postoperative thrombosis after abdominal surgery for cancer. For treatment and secondary prophylaxis, clinical trials have shown that LMWHs are feasible, safe and more effective than oral anticoagulants in preventing recurrent VTE in cancer patients. Nonetheless, formal economic analyses are needed to study the cost-effectiveness of these agents. The preliminary observations that LMWHs are associated with reduction in cancer mortality make these agents an attractive therapeutic option in oncology 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.119
GPT teacher head0.458
Teacher spread0.339 · 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

Citations8
Published2003
Admission routes2
Has abstractyes

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