Pregnancy Outcomes in Women With Primary Systemic Vasculitis [13C]
Bibliographic record
Abstract
INTRODUCTION: This study analyzes pregnancy outcomes in women with large, medium or small vessel vasculitis with the intent of aiding pre-conceptional counselling and informing antepartum and intrapartum care of these women. METHODS: We conducted a retrospective study of women with large-, medium- or small-vessel vasculitis and documented pregnancies between 2001 and 2016, identified from the Special Pregnancy Program Database at Mount Sinai Hospital, Toronto. We obtained information from hospital records and analyzed maternal, fetal/neonatal and obstetric outcomes between the different vasculitides. RESULTS: We identified 60 pregnancies in 50 women including 10 with large-vessel vasculitis (Takayasu’s arteritis), five with medium-sized vessel vasculitis, 30 with small-vessel vasculitis (16 ANCA-associated vasculitis, 14 with other such as IgA vasculitis), three with central nervous system vasculitis and two with retinal vasculitis. Vasculitis flares occurred in women with large-vessel (3/12), small-vessel (13/36) and retinal (2/3) vasculitis. Pregnancy complication rates were low, with one case each of first-trimester miscarriage, congenital anomaly, stillbirth and gestational diabetes. Although seven (26.4%) viable pregnancies resulted in preterm birth, the mean gestational age, regardless of the type of vasculitis, was over 35 weeks of gestation. Fetal growth restriction only occurred with small-vessel vasculitis (10 newborns, 28.6%). Of the 44 deliveries, 22 (50%) were spontaneous vaginal deliveries. All caesarean (18, 40.9%) and assisted vaginal (7, 15.9%) deliveries were for obstetric indications. CONCLUSION: With multidisciplinary management, women with vasculitis can anticipate excellent maternal and fetal outcomes, although they are at increased risk for late preterm birth.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".