Risk of recurrent venous thromboembolism according to malignancy characteristics in patients with cancer-associated thrombosis: a systematic review of observational and intervention studies
Bibliographic record
Abstract
Patients with cancer-associated venous thromboembolism (VTE) should be treated with low molecular weight heparin. The ideal duration of anticoagulation in this population is unknown. It is important to evaluate whether there is variation in susceptibility for recurrent VTE according to malignancy characteristics. In this systematic review we sought to evaluate cancer characteristics that may influence the risk for VTE recurrence and the success of anticoagulation in patients with cancer-associated VTE. A systematic literature search strategy identified potential studies on MEDLINE, Embase, the Cochrane Register of Controlled Trials, MEDLINE In-Process and other nonindexed citations using the Ovid interface. There was no restriction to study design or language. No randomized controlled trials fulfilled our inclusion criteria. We included four retrospective and six prospective studies. VTE recurrence rate according to tumour stage suggested an increased risk for patients with metastatic malignancy compared with patients with localized disease (relative risk 1.36; 95% confidence interval 1.06-1.74, P = 0.01). We were unable to pool data to evaluate VTE recurrence according to tumour site and histology. The isolated evaluation of the included studies suggested that younger patients with adenocarcinoma, lung or gastrointestinal malignancy have the highest risk. There is paucity of data regarding detailed malignancy characteristics in patients with cancer-associated VTE. It appears that metastatic malignancy, or adenocarcinoma, or lung malignancy confers a higher risk of VTE recurrence than patients with localized malignancy, nonadenocarcinoma or breast cancer.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".