Oral anticoagulation in patients with cancer who have no therapeutic or prophylactic indication for anticoagulation
Bibliographic record
Abstract
BACKGROUND: A number of basic research and clinical studies have led to the hypothesis that oral anticoagulants may improve the survival of patients with cancer through an antitumor effect in addition to their antithrombotic effect. OBJECTIVES: To evaluate the efficacy and safety of oral anticoagulants in patients with cancer with no therapeutic or prophylactic indication for anticoagulation. SEARCH STRATEGY: A comprehensive search for studies of anticoagulation in cancer patients including (1) a February 2010 electronic search of the following databases: Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library), MEDLINE, EMBASE, ISI the Web of Science; (2) hand search of the American Society of Clinical Oncology (starting with its first volume, 1982) and of the American Society of Hematology (starting with its 2003 issue); (3) checking of references of included studies; and (4) use of "related article" feature in PubMed. SELECTION CRITERIA: Randomized controlled trials (RCTs) comparing vitamin K antagonist or other oral anticoagulants to no intervention or placebo in cancer patients without clinical evidence of venous thromboembolism. DATA COLLECTION AND ANALYSIS: Using a standardized data form we extracted data on risk of bias, participants, interventions and outcomes of interest that included all cause mortality, venous thromboembolism, major bleeding and minor bleeding. MAIN RESULTS: Of 8187 identified citations, five RCTs fulfilled the inclusion criteria. Warfarin was the oral anticoagulant in all of these RCTs and it was compared to either placebo or no intervention. The quality of evidence was moderate for all outcomes. The effect of warfarin on reduction in mortality was not statistically significant at six months (Relative risk (RR) = 0.96; 95% CI 0.80 to 1.16), at one year (RR = 0.94; 95% CI 0.8 to 1.03) at two years (RR = 0.97; 95% CI 0.87 to 1.08) or at five years (RR 0.91; 95% CI 0.83 to 1.01). One study assessed the effect of warfarin on venous thromboembolism and showed a RR reduction of 85% (P = 0.031). Warfarin increased both major bleeding (RR = 4.24; 95% CI 1.85 to 9.68) and minor bleeding (RR = 3.34; 95% CI 1.66 to 6.74). AUTHORS' CONCLUSIONS: Existing evidence does not suggest a mortality benefit from oral anticoagulation in patients with cancer while increasing the risk for bleeding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".