The Effects of Statins on Respiratory Symptoms and Pulmonary Fibrosis in COVID-19 Patients with Diabetes Mellitus: A Longitudinal Multicenter Study
Bibliographic record
Abstract
The aim of this prospective cohort study was to explore the effect of statins on long-term respiratory symptoms and pulmonary fibrosis in coronavirus disease 2019 (COVID-19) patients with diabetes mellitus (DM). Patients were recruited from three tertiary hospitals, categorized into Statin or Non-statin groups, and assessed on days 0, 28, and 90 after symptoms onset to record the duration of symptoms. Pulmonary fibrosis was scored at baseline and follow-up time points by high-resolution computed tomography scans. Each group comprised 176 patients after propensity score matching. Data analysis revealed that the odds of having cough and dyspnea were significantly higher in the Non-statin group compared to the Statin group during the follow-up period. Overall, there was no significant difference in the change in pulmonary fibrosis score between groups. However, Non-statin patients with > 5 years of DM were more likely to exhibit a significantly higher fibrosis score during the follow-up period as compared to their peers in the Statin group. Our results suggest that the use of statins is associated with a lower risk of developing chronic cough and dyspnea in diabetic patients with COVID-19, and may reduce pulmonary fibrosis associated with COVID-19 in patients with long-term (> 5 years) DM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".