Thromboembolic Disease in Patients With Cancer and COVID-19: Risk Factors, Prevention and Practical Thromboprophylaxis Recommendations–State-of-the-Art
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer and COVID-19 are both well-established risk factors predisposing to thrombosis. Both disease entities are correlated with increased incidence of venous thrombotic events through multifaceted pathogenic mechanisms involving the interaction of cancer cells or SARS-CoV2 on the one hand and the coagulation system and endothelial cells on the other hand. Thromboprophylaxis is recommended for hospitalized patients with active cancer and high-risk outpatients with cancer receiving anticancer treatment. Universal thromboprophylaxis with a high prophylactic dose of low molecular weight heparins (LMWH) or therapeutic dose in select patients, is currentlyindicated for hospitalized patients with COVID-19. Also, prophylactic anticoagulation is recommended for outpatients with COVID-19 at high risk for thrombosis or disease worsening. However, whether there is an additive risk of thrombosis when a patient with cancer is infected with SARS-CoV2 remains unclear In the current review, we summarize and critically discuss the literature regarding the epidemiology of thrombotic events in patients with cancer and concomitant COVID-19, the thrombotic risk assessment, and the recommendations on thromboprophylaxis for this subgroup of patients. Current data do not support an additive thrombotic risk for patients with cancer and COVID-19. Of note, patients with cancer have less access to intensive care unit care, a setting associated with high thrombotic risk. Based on current evidence, patients with cancer and COVID-19 should be assessed with well-established risk assessment models for medically ill patients and receive thromboprophylaxis, preferentially with LMWH, according to existing recommendations. Prospective trials on well-characterized populations do not exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it