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Parenteral anticoagulation in ambulatory patients with cancer

2014· review· en· W4240119833 on OpenAlexaff
Elie A. Akl, Lara A Kahale, Rami A. Ballout, Maddalena Barba, Victor E D Yosuico, Frederiek F van Doormaal, Saskia Middeldorp, Andrew Bryant, Holger J. Schünemann

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2014
Typereview
Languageen
Field
Topic
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAmbulatoryRandomized controlled trialPulmonary embolismThrombosisDeep veinCancerInternal medicineSurgeryIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Anticoagulation may improve survival in patients with cancer through an antitumor effect in addition to the perceived antithrombotic effect. OBJECTIVES: To evaluate the efficacy and safety of parenteral anticoagulants in ambulatory patients with cancer who, typically, are undergoing chemotherapy, hormonal therapy or radiotherapy, but otherwise have no standard therapeutic or prophylactic indication for anticoagulation. SEARCH METHODS: A comprehensive search included (1) an electronic search (February 2013) of the following databases: Cochrane Central Register of Controlled Trials (CENTRAL) (2013, Issue 1), MEDLINE (1966 to February 2013; accessed via OVID) and EMBASE(1980 to February 2013; accessed via OVID); (2) handsearching of conference proceedings; (3) checking of references of included studies; (4) use of the 'related citation' feature in PubMed and (5) a search for ongoing studies. SELECTION CRITERIA: Randomized controlled trials (RCTs) assessing the benefits and harms of parenteral anticoagulation in ambulatory patients with cancer. Typically, these patients are undergoing chemotherapy, hormonal therapy or radiotherapy, but otherwise have no standard therapeutic or prophylactic indication for anticoagulation. DATA COLLECTION AND ANALYSIS: Using a standardized form we extracted data in duplicate on methodological quality, participants, interventions and outcomes of interest including all-cause mortality, symptomatic venous thromboembolism (VTE), symptomatic deep vein thrombosis (DVT), symptomatic pulmonary embolism (PE), arterial thrombosis (e.g. stroke, myocardial infarction), major bleeding, minor bleeding and quality of life. MAIN RESULTS: Of 9559 identified citations, 15 RCTs fulfilled the eligibility criteria. These trials enrolled 7622 participants for whom follow-up data were available. In all included RCTs the intervention consisted of heparin (either unfractionated heparin or low molecular weight heparin). Overall, heparin may have a small effect on mortality at 12 months and 24 months (risk ratio (RR) 0.97; 95% confidence interval (CI) 0.92 to 1.01 and RR 0.95; 95% CI 0.90 to 1.00, respectively). Heparin therapy was associated with a statistically and clinically important reduction in venous thromboembolism (RR 0.56; 95% CI 0.42 to 0.74) and a clinically important increase in the risk of minor bleeding (RR 1.32; 95% 1.02 to 1.71). Results failed to show or to exclude a beneficial or detrimental effect of heparin on major bleeding (RR 1.14; 95% CI 0.70 to 1.85) or quality of life. Our confidence in the effect estimates (i.e. quality of evidence) was high for symptomatic venous thromboembolism, moderate for mortality, major bleeding and minor bleeding, and low for quality of life. AUTHORS' CONCLUSIONS: Heparin may have a small effect on mortality at 12 months and 24 months. It is associated with a reduction in venous thromboembolism and a likely increase in minor bleeding. Future research should further investigate the survival benefit of different types of anticoagulants in patients with different types and stages of cancer. The decision for a patient with cancer to start heparin therapy for survival benefit should balance the benefits and downsides, and should integrate the patient's values and preferences.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0160.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.101
GPT teacher head0.389
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; both teacher heads agree on what is shown here.

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

Citations63
Published2014
Admission routes1
Has abstractyes

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