Therapeutic Anticoagulation in Critically Ill Patients with Covid-19 – Preliminary Report
Bibliographic record
Abstract
Abstract Background Thrombosis may contribute to morbidity and mortality in Covid-19. We hypothesized that therapeutic anticoagulation would improve outcomes in critically ill patients with Covid-19. Methods We conducted an open-label, adaptive, multiplatform, randomized, clinical trial. Patients with severe Covid-19, defined as the requirement for organ support with high flow nasal cannula, non-invasive ventilation, invasive ventilation, vasopressors, or inotropes, were randomized to receive therapeutic anticoagulation with heparin or pharmacological thromboprophylaxis as per local usual care. The primary outcome was an ordinal scale combining in-hospital mortality (assigned –1) and days free of organ support to day 21. Results Therapeutic anticoagulation met the pre-defined criteria for futility in patients with severe Covid-19. The primary outcome was available for 1,074 participants (529 randomized to therapeutic anticoagulation and 545 randomized to usual care pharmacological thromboprophylaxis). Median organ support-free days were 3 days (interquartile range –1, 16) in patients assigned to therapeutic anticoagulation and 5 days (interquartile range –1, 16) in patients assigned to usual care pharmacological thromboprophylaxis (adjusted odds ratio 0.87, 95% credible interval (CrI) 0.70-1.08, posterior probability of futility [odds ratio<1.2] 99.8%). Hospital survival was comparable between groups (64.3% vs. 65.3%, adjusted odds ratio 0.88, 95% CrI 0.67-1.16). Major bleeding occurred in 3.1% of patients assigned to therapeutic anticoagulation and 2.4% of patients assigned to usual care pharmacological thromboprophylaxis. Conclusions In patients with severe Covid-19, therapeutic anticoagulation did not improve hospital survival or days free of organ support compared to usual care pharmacological thromboprophylaxis. Trial registration numbers NCT02735707 , NCT04505774 , NCT04359277 , NCT04372589
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".