Risk assessment for recurrent venous thromboembolism in patients with cancer
Bibliographic record
Abstract
Cancer patients are at high risk for first and recurrent venous thromboembolism (VTE). While various risk factors for a first cancer-associated VTE event have been identified, information on risk factors for recurrent VTE is limited or inconsistent. Therefore, risk assessment for VTE recurrence in cancer patients is challenging at the moment. Certain patient- and tumor-related factors such as presence of metastasis and high VTE risk tumor types, such a pancreas and lung cancer, have been associated with an increased risk of recurrent VTE in patients with cancer. Previously, a risk assessment model, the Ottawa Score, was established to aid in the clinical decision-making regarding duration of anticoagulant therapy; however, its discriminative capacity could not be validated and therefore it is not implemented in clinical practice. There is an urgent call for meeting this medical need and providing tools for improved risk assessment and stratification for recurrent VTE in patients with cancer. In this review, we provide an overview of clinical as well as laboratory markers that influence the recurrence risk of cancer-associated VTE and critically appraise existing risk stratification tools and approaches of integrating risk assessment in clinical decision making.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".