Update from the clinic: what’s new in the diagnosis of cancer-associated thrombosis?
Bibliographic record
Abstract
Malignancy is associated with a high risk of venous thromboembolism (VTE), and treatment with anticoagulant therapy is associated with a high risk of bleeding. Thus, accurate and timely VTE diagnosis in cancer patients is essential for identifying individuals who would benefit from anticoagulant therapy and for avoiding unnecessary treatment that can cause anticoagulant-related bleeding. The approach to the diagnosis of VTE in non-cancer patients involves a stepwise process beginning with an assessment of the pretest probability (PTP) of VTE using a validated clinical prediction rule (CPR) followed by D-dimer testing and/or diagnostic imaging. In patients with a low PTP and a negative D-dimer result, VTE can be excluded without additional imaging. However, published data suggest that CPRs and D-dimer testing may not be as accurate or as useful in patients with cancer. Studies have shown that the combination of a low PTP and negative D-dimer result is not efficient for exclusion of deep vein thrombosis (DVT) or pulmonary embolism (PE) in the cancer patient population because the vast majority of patients still require radiologic imaging. We propose that cancer patients with suspected VTE should proceed directly to radiologic imaging to confirm or exclude a diagnosis of DVT or PE.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".