Active prescription of low-dose aspirin during or prior to hospitalization and mortality in COVID-19: A systematic review and meta-analysis of adjusted effect estimates
Bibliographic record
Abstract
BackgroundThis study aimed to investigate whether the active prescription of low-dose aspirin during or prior to hospitalization affects mortality in patients with coronavirus disease 2019 (COVID-19). Aspirin is often prescribed for secondary prevention in patients with cardiovascular disease and other comorbidities that might increase mortality, and may therefore falsely demonstrate increased mortality. To reduce bias, only studies that performed an adjusted analysis were included in this review.MethodsA systematic literature search of PubMed, Scopus, Embase and Clinicaltrials.gov was performed, from inception until 16 April 2021. The exposure was active prescription of low-dose aspirin during or prior to hospitalization. The primary outcome was mortality. The pooled adjusted effect estimate was reported as relative risk (RR).ResultsSix eligible studies were included in this meta-analysis, comprising 13,993 patients. The studies had low-to-moderate risk of bias based on the Newcastle–Ottawa Scale. The meta-analysis indicated that the use of low-dose aspirin was independently associated with reduced mortality {RR 0.46 [95% confidence interval (CI) 0.35–0.61], P < 0.001; I2 = 36.2%}. Subgroup analysis on in-hospital low-dose aspirin administration also showed a significant reduction in mortality [RR 0.39 (95% CI 0.16–0.96), P < 0.001; I2 = 47.0%].ConclusionUse of low-dose aspirin is independently associated with reduced mortality in patients with COVID-19, with low certainty of evidence.
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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 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".