Thromboprophylaxis for patients with cancer and central venous catheters
Bibliographic record
Abstract
BACKGROUND: Central venous catheter (CVC) placement increases the risk of thrombosis and subsequent death in patients with cancer. The objective of this systematic review was to determine the efficacy and safety of anticoagulation in reducing mortality and thromboembolic events in cancer patients with a CVC. METHODS: The authors searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, and ISI the Web of Science databases. They included randomized controlled trials in patients with cancer comparing unfractionated heparin (UFH), low-molecular-weight heparin (LMWH), vitamin K antagonists, fondaparinux, or ximelagatran with no intervention, placebo, or each other. The standard methods of the Cochrane Collaboration were used for the analyses. RESULTS: Of 3986 identified citations we included 9 randomized clinical trials, none of which evaluated fondaparinux or ximelagatran. Heparin therapy (UFH or LMWH) was associated with a trend toward a reduction in symptomatic deep venous thrombosis (DVT) (relative risk (RR), 0.43; 95% confidence interval (95% CI), 0.18-1.06), but there was no statistically significant effect on mortality (RR, 0.74; 95% CI, 0.40-1.36), infection (RR, 0.91; 95% CI, 0.36-2.28), major bleeding (RR, 0.68; 95% CI, 0.10-4.78), or thrombocytopenia (RR, 0.85; 95% CI, 0.49-1.46). The effect of warfarin on symptomatic DVT also was not statistically significant (RR, 0.62; 95% CI, 0.30-1.27). CONCLUSIONS: The balance of benefits and downsides of thromboprophylaxis in cancer patients with CVC are uncertain. Clinicians together with their patients must weigh these factors carefully when making decisions regarding thromboprophylaxis.
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 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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".