Impact of COVID‐19 in immunosuppressive drug‐naïve autoimmune disorders: Autoimmune gastritis, celiac disease, type 1 diabetes, and autoimmune thyroid disease
Bibliographic record
Abstract
Few conflicting data are currently available on the risk of SARS-CoV-2 infection in patients with autoimmune disorders. The studies performed so far are influenced, in most cases, by the treatment with immunosuppressive drugs, making it difficult to ascertain the burden of autoimmunity per se. For this reason, herein we assessed the susceptibility to COVID-19 in immunosuppressive drug-naïve patients with autoimmune diseases, such as autoimmune gastritis (AIG), celiac disease (CD), type 1 diabetes (T1D), and autoimmune thyroid disease (AITD). Telephone interviews were conducted on 400 patients-100 for each group-in May 2021 by looking at the positivity of molecular nasopharyngeal swabs and/or serology for SARS-CoV-2, the need for hospitalization, the outcome, and the vaccination status. Overall, a positive COVID-19 test was reported in 33 patients (8.2%), comparable with that of the Lombardy general population (8.2%). In particular, seven patients with AIG, 9 with CD, 8 with T1D, and 9 with AITD experienced COVID-19. Only three patients required hospitalization, none died, and 235 (58.7%) were vaccinated, 43 with AIG, 47 with CD, 91 with T1D, and 54 with AITD. These results seem to suggest that autoimmunity per se does not increase the susceptibility to COVID-19. Also, COVID-19 seems to be mild in these patients, as indicated by the low hospitalization rates and adverse outcomes, although further studies are needed to better clarify this issue.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".