Duration of anticoagulant therapy and VTE recurrence in patients with cancer
Bibliographic record
Abstract
PURPOSE: Anticoagulant therapy for at least 3-6 months is currently recommended for treatment of venous thromboembolism (VTE) in patients with cancer, but the optimal duration of treatment is unknown. This study examines the association between the duration of anticoagulation treatment and VTE recurrence in cancer patients. METHODS: The Humana claims database was used to identify newly diagnosed cancer patients who had their first VTE diagnosis between January 1, 2013, and May 31, 2015, and initiated injectable or oral anticoagulant therapy. Follow-up was calculated from the index treatment initiation to the end of eligibility or end of data (June 2015). VTE recurrence was defined as a hospitalization with a primary diagnosis of VTE. Cox proportional hazards models were used to evaluate the risk of VTE recurrence by duration of therapy in patients who discontinued therapy. RESULTS: The study included 1158 patients. Compared to patients treated for 0 to 3 months, VTE recurrences were significantly lower among patients treated for 3 to 6, or over 6 months. After adjustment for baseline characteristics, patients treated for 3 to 6 months (HR [95%CI], 0.53; 0.37-0.76) and more than 6 months (HR [95%CI], 0.48; 0.34-0.68) were still significantly less likely to have VTE recurrences compared to patients treated for 0 to 3 months (both p < 0.01). Findings were similar using a VTE event definition that included outpatient visits. CONCLUSIONS: Among newly diagnosed cancer patients with VTE, anticoagulant therapy lasting more than 3 months was associated with a lower risk of VTE recurrence.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".