COVID-19 and Rheumatic Diseases: It Is Time to Better Understand This Association
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19; caused by the SARS-CoV-2 virus), which by the end of 2019 was completely unknown to clinicians, brought uncertainties and challenges never before experienced in the modern era. Since the World Health Organization declared the pandemic on March 11, 20201, more than 50,000,000 confirmed cases have been reported worldwide, and more than 1,200,000 individuals have died from the disease2. In this scenario, many clinical questions have emerged from a rheumatologic standpoint: Are patients with immune-mediated rheumatic diseases (IMRD) more likely to get infected by SARS-CoV-2? Will patients with rheumatic diseases develop more severe forms of COVID-19? How should we manage immunosuppressors and biological therapy? Is there a chance of reactivation of IMRD after COVID-19? Will a SARS-CoV-2 infection trigger an autoimmune disease? To date, these questions remain unanswered. Although patients with IMRD are known to be at higher risk of infection—attributed mainly to disease activity, comorbidities, and immunosuppressive therapy—the first published papers addressing COVID-19 in patients with IMRD, based on the clinical information published up to that time, indicated there was no consistent evidence that these patients were at higher risk compared to those with other comorbidities3,4. Since then, numerous papers about COVID-19 in patients with IMRD have been published, but there are still many unanswered questions. The first question that has not yet been fully answered is related to the prevalence of COVID-19 …
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".