CATCH: A randomized trial comparing tinzaparin versus warfarin for treatment of acute venous thromboembolism (VTE) in cancer patients.
Bibliographic record
Abstract
TPS9149^ Background: VTE is a major cause of morbidity and mortality in cancer patients. LMWHs have been shown to be superior to warfarin in one randomized study, but adequately powered confirmatory studies have not been conducted and warfarin continues to be widely used for treatment of cancer-associated VTE. Methods: We are conducting an open-label, randomized trial of tinzaparin versus warfarin in 900 patients with active cancer and symptomatic proximal deep vein thrombosis (DVT) and/or pulmonary embolism (PE). Tinzaparin is given at full treatment doses (175 IU/kg once daily) for 6 months in the experimental arm and initial tinzaparin treatment for 5-10 days followed by dose-adjusted warfarin (target INR 2.0-3.0) is given for 6 months in the control arm. The primary composite outcome is time to recurrent VTE event, including incidentally diagnosed VTE and fatal PE. Baseline characteristics will be analysed for their ability to predict the risk for recurrent VTE or bleeding. In particular, the parameters of the Khorana scale and Wells rule will be tested for their usefulness in predicting recurrent VTE. Predictive biomarkers will be tested including D-dimer and Tissue Factor. Assessment of post-thrombotic syndrome (PTS), quality of life and healthcare resource utilization will also be performed. The trial is recruiting in > 160 sites in >25 countries in 4 continents (NCT01130025). As of Jan 2012, 135 sites were activated for study enrolment and 228 patients have been enrolled. We anticipate completion of enrolment in Jan 2013. The results obtained from this study will add significantly to the knowledge on the efficacy, safety and cost-effectiveness of LMWH to prevent recurrent VTE. Important prospective data on the clinical significance of incidental VTE in patients with active cancer will be generated, and analyses of risk stratification parameters will add important information that may help to further tailor therapy. The study of PTS, which has not previously been done in this selected patient population, will add to the evidence that tinzaparin significantly reduces the incidence of PTS and leg ulcers (Hull et al, Am J Med 2009;122:762-9).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".