The Efficacy and Safety of Low Molecular Weight Heparin Administration to Improve Survival of Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Low molecular weight heparins (LMWH) are often used as a first-line therapy for the prevention of thrombosis in cancer patients. Preclinical evidence from animal models suggests that LMWH may have antimetastatic properties. Clinical evidence of this effect is inconclusive. The objective of this systematic review is to evaluate the effect of LMWH on overall survival in patients with solid tumor malignancies. METHODS: MEDLINE, Embase, and The Cochrane Central Register of Controlled trials were searched from inception to November 26, 2018. We included randomized controlled trials that compared LMWH to placebo, a no-treatment arm, or a short-term prophylactic course of LMWH in adult patients with solid tumors. The primary outcome was overall survival. Secondary outcomes included progression-free survival, the occurrence of venous thromboembolism, and major bleeding events. The risk of bias was assessed in duplicate using the Cochrane Risk-of-Bias tool. RESULTS: defined subgroup analyses, the effect was not shown to vary by the type of LMWH, duration of LMWH use, length of study follow-up, comparator used in the study, or the setting in which the LMWH was administered. The majority of studies had an unclear risk of bias for at least one methodological criterion. CONCLUSION: Although LMWH is thought to possess antimetastatic properties and thus have the potential to improve survival in cancer patients, existing data do not support this hypothesis.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".