Randomized trials of therapeutic heparin for COVID‐19: A meta‐analysis
Bibliographic record
Abstract
Background Pulmonary endothelial injury and microcirculatory thromboses likely contribute to hypoxemic respiratory failure, the most common cause of death, in patients with COVID-19. Randomized controlled trials (RCTs) suggest differences in the effect of therapeutic heparin between moderately and severely ill patients with COVID-19. We did a systematic review and meta-analysis of RCTs to determine the effects of therapeutic heparin in hospitalized patients with COVID-19. Methods We searched PubMed, Embase, Web of Science, medRxiv, and medical conference proceedings for RCTs comparing therapeutic heparin with usual care, excluding trials that used oral anticoagulation or intermediate doses of heparin in the experimental arm. Mantel-Haenszel fixed-effect meta-analysis was used to combine odds ratios (ORs). Results and Conclusions There were 3 RCTs that compared therapeutic heparin to lower doses of heparin in 2854 moderately ill ward patients, and 3 RCTs in 1191 severely ill patients receiving critical care. In moderately ill patients, there was a nonsignificant reduction in all-cause death (OR, 0.76; 95% CI, 0.57-1.02), but significant reductions in the composite of death or invasive mechanical ventilation (OR, 0.77; 95% CI, 0.60 0.98), and death or any thrombotic event (OR, 0.58; 95% CI, 0.45-0.77). Organ support-free days alive (OR, 1.29; 95% CI, 1.07-1.57) were significantly increased with therapeutic heparin. There was a nonsignificant increase in major bleeding. In severely ill patients, there was no evidence for benefit of therapeutic heparin, with significant treatment-by-subgroup interactions with illness severity for all-cause death (P = .034). In conclusion, therapeutic heparin is beneficial in moderately ill patients but not in severely ill patients hospitalized 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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".