The Use of Therapeutic-Dose Anticoagulation and Its Effect on Mortality in Patients With COVID-19: A Systematic Review
Bibliographic record
Abstract
The incidence of venous thromboembolism (VTE) events in patients with COVID-19 treated with a standard thromboprophylaxis dose of anticoagulants remains high. We conducted a systematic review in order to explore the association between therapeutic-dose anticoagulation and its effect on mortality in patients with COVID-19. A systematic search was carried out using the electronic databases of PubMed, EuropePMC, and the Cochrane Central Database, using specific keywords. All articles that fulfilled the inclusion criteria were included in the qualitative analysis. There were 8 observational studies included in the final qualitative analysis. Quality assessment using the Newcastle-Ottawa Scale (NOS) showed a mean score of 7.5 ± 1.06, indicating moderate to high quality of the studies. Three retrospective cohort studies reported a reduction in the mortality rate, while 6 other studies showed no mortality benefits among patients with COVID-19 treated with therapeutic-dose anticoagulation. There was a slight tendency toward a reduction in the mortality rate among mechanically-ventilated patients with COVID-19 receiving therapeutic-dose anticoagulation. Bleeding events and thrombotic complications among patients receiving therapeutic-dose anticoagulation were reported in 3 studies. Although it is too soon to draw any conclusions, this systematic review draws attention to current evidence regarding the association between therapeutic-dose anticoagulation and its effect on mortality in patients with COVID-19.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".