Real‐world effectiveness of Azvudine versus nirmatrelvir–ritonavir in hospitalized patients with COVID‐19: A retrospective cohort study
Bibliographic record
Abstract
Chinese guidelines prioritize the use of Azvudine and nirmatrelvir-ritonavir in COVID-19 patients. Nevertheless, the real-world effectiveness of Azvudine versus nirmatrelvir-ritonavir is still lacking, despite clinical trials showing their effectiveness compared with matched controls. To compare the effectiveness of Azvudine versus nirmatrelvir-ritonavir treatments in real-world clinical practice, we identified 2118 hospitalized COVID-19 patients, with a follow-up of up to 38 days. After exclusions and propensity score matching, we included 281 Azvudine recipients and 281 nirmatrelvir-ritonavir recipients who did not receive oxygen therapy at admission. The lower crude incidence rate of composite disease progression outcome (7.83 vs. 14.83 per 1000 person-days, p = 0.026) and all-cause death (2.05 vs. 5.78 per 1000 person-days, p = 0.052) were observed among Azvudine recipients. Azvudine was associated with lower risks of composite disease progression outcome (hazard ratio [HR]: 0.55; 95% confidence interval [CI]: 0.32-0.94) and all-cause death (HR: 0.40; 95% CI: 0.16-1.04). In subgroup analyses, the results of composite outcome retained significance among patients aged <65 years, those having a history of disease, those with severe COVID-19 at admission, and those receiving antibiotics. These findings suggest that Azvudine treatment showed effectiveness in hospitalized COVID-19 patients compared with nirmatrelvir-ritonavir in terms of composite disease progression outcome.
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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 | 0.009 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".