Venous thromboembolism risk, prophylaxis and management in cancer patients with COVID-19: An unmet medical need
Bibliographic record
Abstract
Cancer patients exhibit an increased risk of venous thromboembolism (VTE), with VTE being the second leading cause of morbidity and mortality in these patients. The implementation of lockdowns following the COVID-19 pandemic has resulted in decreased mobility and delayed access to care, thus further increasing the susceptibility to VTE. Cancer patients may also be at a higher risk of SARS-CoV-2 infection and have been shown to be more likely to experience severe COVID-19 disease compared to patients without cancer. Given that both cancer and COVID-19 exhibit a hypercoagulable state, stasis of blood flow, and endothelial injury, cancer patients with COVID-19 constitute a vulnerable population with a high risk of thrombosis and bleeding. However, to date there are limited studies evaluating whether cancer patients infected with SARS-CoV-2 have a higher VTE incidence than COVID-19 patients without cancer, how to assess the risk of VTE, prophylaxis and treatment in this special population. Herein, we highlight the urgent need for studies in cancer patients with COVID-19 to ensure appropriate patient care and improve clinical outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".