Therapeutic-dose heparin combined with antiplatelets in noncritically ill patients with COVID-19: a secondary analysis of a multiplatform randomized controlled trial
Bibliographic record
Abstract
Background: Therapeutic-dose heparin improves outcomes in noncritically ill patients hospitalized for COVID-19. The effect of antiplatelet exposure in addition to therapeutic-dose heparin is unknown. Objectives: To evaluate the effect of antiplatelet exposure in addition to therapeutic-dose heparin on survival without organ support. Methods: We conducted an observational secondary analysis of a multiplatform randomized controlled trial, analyzing noncritically ill patients hospitalized for COVID-19 who received an antiplatelet agent (acetylsalicylic acid or P2Y12 inhibitor) and therapeutic-dose heparin (combination) compared with therapeutic-dose heparin alone (control). We used a 3-level ordinal primary outcome: (1) survival without organ support, (2) survival with organ support, and (3) mortality by day 21. Propensity scores were estimated using logistic regression. Balanced analytic groups were established using stabilized inverse probability of treatment weighting. A proportional odds model was used to estimate the effect of antiplatelet exposure. Results: Among 1021 patients, 194 (19.0%) were exposed to an antiplatelet (95.4% acetylsalicylic acid) and therapeutic-dose heparin. All patients were used to calculate the propensity scores and stabilized weights. After applying inverse probability of treatment weighting, the effective sample size was 60 in the combination group and 652 in the control group. Means and prevalences of continuous and dichotomous variables were similar between groups, with no evidence of misclassification. Exposure to an antiplatelet was not associated with improved survival without organ support (76.3% vs 80.5%; odds ratio, 1.07; 95% CI, 0.71-1.64). Conclusion: In noncritically ill patients hospitalized for COVID-19 receiving therapeutic-dose heparin, exposure to an antiplatelet agent was not associated with improved survival without organ support.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".