Effects of immunosuppressive drugs on COVID‐19 severity in patients with autoimmune hepatitis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: We investigated associations between baseline use of immunosuppressive drugs and severity of Coronavirus Disease 2019 (COVID-19) in autoimmune hepatitis (AIH). PATIENTS AND METHODS: Data of AIH patients with laboratory confirmed COVID-19 were retrospectively collected from 15 countries. The outcomes of AIH patients who were on immunosuppression at the time of COVID-19 were compared to patients who were not on AIH medication. The clinical courses of COVID-19 were classified as (i)-no hospitalization, (ii)-hospitalization without oxygen supplementation, (iii)-hospitalization with oxygen supplementation by nasal cannula or mask, (iv)-intensive care unit (ICU) admission with non-invasive mechanical ventilation, (v)-ICU admission with invasive mechanical ventilation or (vi)-death and analysed using ordinal logistic regression. RESULTS: We included 254 AIH patients (79.5%, female) with a median age of 50 (range, 17-85) years. At the onset of COVID-19, 234 patients (92.1%) were on treatment with glucocorticoids (n = 156), thiopurines (n = 151), mycophenolate mofetil (n = 22) or tacrolimus (n = 16), alone or in combinations. Overall, 94 (37%) patients were hospitalized and 18 (7.1%) patients died. Use of systemic glucocorticoids (adjusted odds ratio [aOR] 4.73, 95% CI 1.12-25.89) and thiopurines (aOR 4.78, 95% CI 1.33-23.50) for AIH was associated with worse COVID-19 severity, after adjusting for age-sex, comorbidities and presence of cirrhosis. Baseline treatment with mycophenolate mofetil (aOR 3.56, 95% CI 0.76-20.56) and tacrolimus (aOR 4.09, 95% CI 0.69-27.00) were also associated with more severe COVID-19 courses in a smaller subset of treated patients. CONCLUSION: Baseline treatment with systemic glucocorticoids or thiopurines prior to the onset of COVID-19 was significantly associated with COVID-19 severity in patients with AIH.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it