Effectiveness and safety of anticoagulants for the treatment of venous thromboembolism in patients with cancer
Bibliographic record
Abstract
Anticoagulation is used to treat venous thromboembolism (VTE) in cancer patients, but may be associated with an increased risk of bleeding. VTE recurrence and major bleeding were assessed in cancer patients treated for VTE with the most currently prescribed anticoagulants in clinical practice. Newly diagnosed cancer patients (first VTE 1/1/2013-05/31/2015) who initiated rivaroxaban, low-molecular-weight heparin (LMWH), or warfarin were identified from Humana claims data and observed until end of eligibility or end of data availability. VTE recurrence was a hospitalization with a primary diagnosis of VTE ≥7 days after first VTE. Major bleeding events on treatment were identified using validated criteria. Cohorts were compared using Kaplan-Meier rates at 6 and 12 months and Cox proportional hazards models. Cohorts were adjusted for their differences at baseline. A total of 2428 patients (rivaroxaban: 707; LMWH: 660; warfarin: 1061) met inclusion criteria. Patient characteristics were well balanced after weighting. There was a trend for lower VTE recurrence rates in rivaroxaban users compared to LMWH users at 6 months (13.2% vs. 17.1%; P = .060) and significantly lower at 12 months (16.5% vs. 22.2%; P = .030) [HR: 0.72, 95% CI: (0.52-0.95); P = .024]. VTE recurrence rates were also lower for rivaroxaban than warfarin users at 6 months (13.2% vs. 17.5%; P = .014) and 12 months (15.7% vs. 19.9%; P = .017) [HR: 0.74, 95% CI: (0.56-0.96); P = .028]. Major bleeding rates were similar across cohorts. This real-world analysis suggests cancer patients with VTE treated with rivaroxaban had significantly lower risk of recurrent VTE and similar risk of bleeding compared to those treated with LMWH or warfarin.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".