Therapeutic Anticoagulation in Non-Critically Ill Patients with Covid-19
Bibliographic record
Abstract
Abstract Background Thrombo-inflammation may contribute to morbidity and mortality in Covid-19. We hypothesized that therapeutic-dose anticoagulation may improve outcomes in non-critically ill patients hospitalized for Covid-19. Methods In an open-label adaptive multiplatform randomized controlled trial, non-critically ill patients hospitalized for Covid-19, defined by the absence of critical care-level organ support at enrollment, were randomized to a pragmatic strategy of therapeutic-dose anticoagulation with heparin or usual care pharmacological thromboprophylaxis. The primary outcome combined survival to hospital discharge and days free of organ support through 21 days, which was evaluated with Bayesian statistical models according to baseline D-dimer. Results The trial was stopped when prespecified criteria for superiority were met for therapeutic-dose anticoagulation in groups defined by high (≥2-fold elevated) and low (<2-fold elevated) D-dimer. Among 2219 participants in the final analysis, the probability that therapeutic anticoagulation increased organ support-free days compared to thromboprophylaxis was 99.0% (adjusted odds ratio 1.29, 95% credible interval 1.04 to 1.61). The adjusted absolute increase in survival to hospital discharge without organ support with therapeutic-dose anticoagulation was 4.6% (95% credible interval 0.7 to 8.1). In the primary adaptive stopping groups, the final probabilities of superiority for therapeutic anticoagulation were 97.3% in the high D-dimer group and 92.9% in the low D-dimer group. Major bleeding occurred in 1.9% and 0.9% of participants randomized to therapeutic anticoagulation and thromboprophylaxis, respectively. Conclusions In non-critically ill patients with Covid-19, an initial strategy of therapeutic-dose anticoagulation with heparin increases the probability of survival to hospital discharge with reduced use of organ support. Trial registration numbers: NCT02735707 , NCT04505774 , NCT04359277 , NCT04372589
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 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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".