The prognostic role of anticoagulants in COVID-19 patients: national COVID-19 cohort in South Korea
Bibliographic record
Abstract
BACKGROUND: There currently exists a paucity of data on whether pre-admission anticoagulants use may have benefits among COVID-19 patients by preventing COVID-19 associated thromboembolism. The aim of this study was to assess the association between pre-admission anticoagulants use and COVID-19 adverse outcomes. METHODS: We conducted a population-based cohort studying using the Health Insurance Review and Assessment Service (HIRA) claims data released by the South Korean government. Our study population consisted of South Koreans who were aged 40 years or older and hospitalized with COVID-19 between 1 January 2020 through 15 May 2020. We defined anticoagulants users as individuals with inpatient and outpatient prescription records in 120 days before cohort entry. Our primary endpoint was a composite of all-cause death, intensive care unit (ICU) admission, and mechanical ventilation use. Individual components of the primary endpoint were secondary endpoints. We compared the risk of endpoints between the anticoagulants users and non-users by logistic regression models, with the standardized mortality ratio weighting (SMRW) adjustment. RESULTS: In our cohort of 4,349 patients, for the primary endpoint of mortality, mechanical ventilation and ICU admission, no difference was noted between anticoagulants users and non-users (SMRW OR 1.11, 95% CI: 0.60-2.05). No differences were noted, among individual components. No effect modification was observed by age, sex, history of atrial fibrillation and thromboembolism, and history of cardiovascular disease. When applying the inverse probability of treatment weighting (IPTW) and SMRW with doubly robust methods in sensitivity analysis, anticoagulants use was associated with increased odds of the primary endpoint. CONCLUSIONS: Pre-admission anticoagulants were not determined to have a protective role against severe COVID-19 outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".