Malignancy and venous thrombosis in the critical care patient
Bibliographic record
Abstract
Venous thromboembolic disease has significant clinical consequences. There are few data available to guide its management in the critically ill cancer patient, perhaps the most complex and challenging patient population encountered. Multiple interacting and often unique factors contribute to both the thrombotic and bleeding risk in such patients. Anticoagulants are effective for prophylaxis and treatment; heparins are the best-studied agents in this setting. Whether unfractionated or low-molecular-weight heparin is the most appropriate agent depends on the exact clinical situation. Prevention of venous thrombosis is a well-recognized health priority, but thromboprophylaxis remains underused, especially in some high-risk populations such as cancer patients. Enhanced recognition of the thrombotic risk factors and a better understanding of the risks and benefits of anticoagulant therapy are necessary to improve utilization, and much research is needed to address how to implement effective thromboprophylaxis strategies. Careful consideration of the patient's overall prognosis is necessary to develop safe, effective, and individualized approaches to treating thrombosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".