Reproductive Outcomes for Women With Vasculitis
Bibliographic record
Abstract
OBJECTIVE: There are limited data on the reproductive health of women with vasculitis. This study used a prospective, international vasculitis pregnancy registry to survey women during and after pregnancy. METHODS: The Vasculitis Pregnancy Registry (VPREG) is imbedded within the Vasculitis Patient-Powered Research Network, an international online research infrastructure. Any pregnant woman with a diagnosis of vasculitis can self-enroll. After enrollment, women are invited to complete online surveys at study entry, once per trimester, and postpartum. Descriptive statistics are reported here. RESULTS: Between 2015 and 2022, 147 women with 149 pregnancies enrolled in VPREG from 16 countries. Data on 78 pregnancies with known outcomes were included in this analysis. During pregnancy, women on average experienced low levels of pain related to vasculitis (scale 0-10, median 2 [IQR 1-5]) and preserved feelings of wellness (scale 0-10, median 3 [IQR 1-5]). Thirty-six percent of women reported their vasculitis was active during pregnancy. Of the 14 women requiring hospitalization during pregnancy outside of delivery, 4 cited active vasculitis as the indication. Most women (54/73, 74%) were prescribed medications for vasculitis during pregnancy. Seventy-six (97%) pregnancies resulted in live births, with 64% delivering vaginally and 21% experiencing a preterm delivery. CONCLUSION: These results demonstrate that most women with vasculitis can experience pregnancies that result in live births delivered at term. During pregnancy, a minority of women reported flares of vasculitis or the need for hospitalization due to vasculitis. These data are useful to rheumatologists and patients to inform and facilitate discussions about reproductive health and vasculitis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".