COVID-19 associated autoimmunity is a feature of severe respiratory disease - a Bayesian analysis
Bibliographic record
Abstract
Abstract Background Serological and clinical features with similarities to systemic autoimmunity have been reported in severe COVID-19, but there is a lack of studies that include contemporaneous controls who do not have COVID-19. Methods Observational cohort study of adult patients admitted to an intensive care unit with acute respiratory failure. Patients were divided into COVID + and COVID − based on SARS-CoV-2 PCR from nasopharyngeal swabs and/or endotracheal aspirates. No COVID-19 specific interventions were given. The primary clinical outcome was death in the ICU within 3 months; secondary outcomes included in-hospital death and disease severity measures. Measurements including autoantibodies, were done longitudinally. ANOVA and Fisher’s exact test were used with α=0.05, with a false discovery rate of q=0.05. Bayesian analysis was performed to provide credible estimates of the possible states of nature compatible with our results. Results 22 COVID + and 20 COVID − patients were recruited, 69% males, median age 60.5 years. Overall, 64% had anti-nuclear antibodies, 38% had antigen-specific autoantibodies, 31% had myositis related autoantibodies, and 38% had high levels of anti-cytokine autoantibodies. There were no statistically significant differences between COVID + and COVID − for any of the clinical or autoantibody parameters. A specific pattern of anti-nuclear antibodies was associated with worse clinical severity for both cohorts. Conclusions Severe COVID + patients have similar humoral autoimmune features as comparably ill COVID − patients, suggesting that autoantibodies are a feature of critical illness regardless of COVID-19 status. The clinical significance of autoimmune serology and the correlation with severity in critical illness remains to be elucidated.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".