The Effect of COVID-19 Illness on Pregnant Patients With Rheumatic Disease: Early Reassuring Data
Bibliographic record
Abstract
As of September 20, 2021, the World Health Organization (WHO) reported 228,206,384 cases of coronavirus disease 2019 (COVID-19), with over 4.5 million deaths worldwide.1 International responses by healthcare providers (HCPs), medical and pharmacologic researchers, and public health workers identified risk factors for serious illness and developed novel therapies and vaccines in real time, even as new variants emerge. Every HCP has been affected, even those not providing direct care to COVID-19–infected patients. Rheumatologists have endeavored to characterize the intersection of COVID-19 with rheumatic diseases (RDs) as well as with immunosuppressive medications. To provide effective care and counsel to patients, we have all answered innumerable patient questions throughout the pandemic, our responses most often based on ongoing research from colleagues. One group, the COVID-19 Global Rheumatology Alliance (C19-GRA), was established within days of the WHO global pandemic declaration on March 11, 2020, and, along with other important research groups, has helped provide answers to some of these important questions. Analysis of C19-GRA physician-reported registry data identified risk factors for hospitalization of COVID-19–infected patients with RD, including prednisone > 10 mg daily (OR 2.05, 95% CI 1.06–3.96), and showed reduced odds of hospitalization with anti–tumor necrosis factor (TNF) agents (OR 0.40, 95% CI 0.19 – 0.81).2 Increased risk of death was associated with older age, male sex, and specific comorbidities as identified in non-RD patients, but also rheumatology-specific factors of moderate–high disease activity and certain medications.3 The death rate in the group of 3729 patients with RD was 10.5%,3 with poorer COVID-19 outcomes in ethnic minorities in the US.4 Other authors have presented analyses of electronic medical record data that suggest RD patients with COVID-19 illness may be at higher risk of hospitalization, intensive care unit (ICU) admission, acute renal failure, and venous thromboembolism when compared to … Address correspondence to Dr. L.R. Sammaritano, Professor of Clinical Medicine, Weill Cornell Medicine, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA. Email: sammaritanol{at}hss.edu.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".