Challenges in evaluating treatments for COVID-19: The case of in-hospital anticoagulant use and the risk of adverse outcomes
Bibliographic record
Abstract
Anticoagulants are a potential treatment for the thrombotic complications resulting from COVID-19. We aimed to determine the association between anticoagulant use and adverse outcomes among hospitalized patients with COVID-19. We used data from the COVID-19 International Collaborative Research Project in South Korea from January to June 2020. We defined exposure using an intention-to-treat approach, with person-time classified as use or non-use of anticoagulants at cohort entry, and a time-varying approach. The primary outcome was all-cause, in-hospital mortality; the secondary outcome was a composite including respiratory outcomes, cardiovascular outcomes, venous thromboembolism, major bleeding, and intensive care unit admission. Cox proportional hazards models estimated adjusted hazard ratios (HRs) of the outcomes comparing use versus non-use of anticoagulants. Our cohort included 2,677 hospitalized COVID-19 patients, of whom 24 received anticoagulants at cohort entry. Users were older and had more comorbidities. The crude incidence rate (per 1,000 person-days) of mortality was 5.83 (95% CI: 2.80, 10.72) among anticoagulant users and 1.36 (95% CI: 1.14, 1.59) for non-users. Crude rates of the composite outcome were 3.20 (95% CI: 1.04, 7.47) and 1.80 (95% CI: 1.54, 2.08), respectively. Adjusted HRs for mortality (HR: 1.12, 95% CI: 0.48, 2.64) and the composite outcome (HR: 0.79, 95% CI: 0.28, 2.18) were inconclusive. Although our study was not able to draw conclusions on anticoagulant effectiveness for COVID-19 outcomes, these results can contribute to future knowledge syntheses of this important question. Our study demonstrated that the dynamic pandemic environment may have important implications for observational studies of COVID-19 treatment effectiveness.
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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 | 0.213 | 0.400 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".