Autoantibodies in COVID-19: implications for disease severity and clinical outcomes
Bibliographic record
Abstract
Few pathogens have historically been subjected to as intense scientific and clinical scrutiny as SARS-CoV-2. The genetic, immunological, and environmental factors influencing disease severity and post-infection clinical outcomes, known as correlates of immunity, remain largely undefined. Clinical outcomes of SARS-CoV-2 infection vary widely, ranging from asymptomatic cases to those with life-threatening COVID-19 symptoms. While most infected individuals return to their former health and fitness within a few weeks, some develop debilitating chronic symptoms, referred to as long-COVID. Autoimmune responses have been proposed as one of the factors influencing long-COVID and the severity of SARS-CoV-2 infection. The association between viral infections and autoimmune pathologies is not new. Viruses such as Epstein-Barr virus and cytomegalovirus, among others, have been shown to induce the production of autoantibodies and the onset of autoimmune conditions. Given the extensive literature on SARS-CoV-2, here we review current evidence on SARS-CoV-2-induced autoimmune pathologies, with a focus on autoantibodies. We closely examine mechanisms driving autoantibody production, particularly their connection with disease severity and long-COVID.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".