Incidence of Venous Thromboembolism in Hospitalized COVID-19 Patients Receiving Thromboprophylaxis
Bibliographic record
Abstract
Background: The purpose of this study was to investigate the association between anticoagulant dosing intensity in coronavirus disease 2019 (COVID-19) infected patients and its outcomes on venous thromboembolism (VTE) and all-cause mortality. Methods: This is a retrospective observational study that examined different anticoagulation regimens among COVID-19 patients for prophylaxis of VTE. Primary outcomes of the study were VTE incidence and all-cause mortality for patients receiving prophylaxis-intensity (PPX) and therapeutic-intensity (TX) anticoagulation. Secondary outcomes were incidence of hemorrhagic events and hospital length of stay. Patients were matched (1:1) based on age and Charlson comorbidity score. Sub-group analyses evaluated outcomes within critically ill patients, between specific anticoagulant agents and comorbid conditions. Results: The primary outcome of VTE occurred in six patients within the prophylactic dose group and eight patients in the therapeutic-intensity dose group (risk ratio (RR): 2.02 (95% confidence interval (CI): 0.7 - 5.2); P = 0.2). Bleeding occurred in 15 (11%) patients in the prophylactic group and 27 (19%) patients in the therapeutic group (RR: 0.5 (95% CI: 0.3 - 1.0); P < 0.049). Hospital length of stay was shorter by 4 days in those treated with prophylactic-intensity anticoagulation (P = 0.003). Intensive care unit admission and ventilation were negatively correlated with mortality in a multivariate analysis. Conclusions: Among hospitalized COVID-19 patients, the use of therapeutic-intensity anticoagulation did not show any benefits in reducing the occurrence of VTE. An increase in mortality and in the incidence of hemorrhagic events was statistically significant in the therapeutic-intensity group. Future prospective studies are warranted to evaluate anticoagulation therapy in COVID-19 infected patients.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".