Association Between Ursodeoxycholic Acid and Clinical Outcomes in Patients With COVID-19 Infection: Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Several studies have investigated the relationship between ursodeoxycholic acid (UDCA) and COVID-19 infection. However, complex and conflicting results have generated confusion in the application of these results. OBJECTIVE: We aimed to investigate whether the association between UDCA and COVID-19 infection can also be demonstrated through the analysis of a large-scale cohort. METHODS: This retrospective study used local and nationwide cohorts, namely, the Jeonbuk National University Hospital into the Observational Medical Outcomes Partnership common data model cohort (JBUH CDM) and the Korean National Health Insurance Service claim-based database (NHIS). We investigated UDCA intake and its relationship with COVID-19 susceptibility and severity using validated propensity score matching. RESULTS: Regarding COVID-19 susceptibility, the adjusted hazard ratio (aHR) value of the UDCA intake was significantly lowered to 0.71 in the case of the JBUH CDM (95% CI 0.52-0.98) and was significantly lowered to 0.93 (95% CI 0.90-0.96) in the case of the NHIS. Regarding COVID-19 severity, the UDCA intake was found to be significantly lowered to 0.21 (95% CI 0.09-0.46) in the case of JBUH CDM. Furthermore, the aHR value was significantly lowered to 0.77 in the case of NHIS (95% CI 0.62-0.95). CONCLUSIONS: Using a large-scale local and nationwide cohort, we confirmed that UDCA intake was significantly associated with reductions in COVID-19 susceptibility and severity. These trends remained consistent regardless of the UDCA dosage. This suggests the potential of UDCA as a preventive and therapeutic agent for COVID-19 infection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".