COVID-19 in Pregnant Women With Rheumatic Disease: Data From the COVID-19 Global Rheumatology Alliance
Bibliographic record
Abstract
OBJECTIVE: To describe coronavirus disease 2019 (COVID-19) and pregnancy outcomes in patients with rheumatic disease who were pregnant at the time of infection. METHODS: Since March 2020, the COVID-19 Global Rheumatology Alliance has collected cases of patients with rheumatic disease with COVID-19. We report details of pregnant women at the time of COVID-19 infection, including obstetric details separately ascertained from providers. RESULTS: We report on 39 patients, including 22 with obstetric detail available. The mean and median age was 33 years, range 24-45 years. Rheumatic disease diagnoses included rheumatoid arthritis (n = 9), systemic lupus erythematosus (n = 9), psoriatic arthritis/other inflammatory arthritides (n = 8), and antiphospholipid syndrome (n = 6). Most had a term birth (16/22), with 3 preterm births, 1 termination, and 1 miscarriage; 1 woman had yet to deliver at the time of report. One-quarter (n = 10/39) of pregnant women were hospitalized following COVID-19 diagnosis. Two of 39 (5%) required supplemental oxygen (both hospitalized); no patients died. The majority did not receive specific medication treatment for their COVID-19 (n = 32/39, 82%), and 7 patients received some combination of antimalarials, colchicine, anti-interleukin 1β, azithromycin, glucocorticoids, and lopinavir/ritonavir. CONCLUSION: Women with rheumatic diseases who were pregnant at the time of COVID-19 had favorable outcomes. These data have limitations due to the small size and methodology; however, they provide cautious optimism for pregnancy outcomes for women with rheumatic disease particularly in comparison to the increased risk of poor outcomes that have been reported in other series of pregnant women with 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".