Pregnancy Outcomes in Anti Neutrophil Cytoplasmic Antibody-Associated Vasculitis: A Systematic Review [37L]
Bibliographic record
Abstract
INTRODUCTION: Information on pregnancy outcomes in Anti Neutrophil Cytoplasmic Antibody (ANCA)-associated vasculitis is limited to small case series. We systematically reviewed the literature to determine pregnancy outcomes in women with ANCA-associated vasculitis, to help with counselling and management of these women. METHODS: We searched Medline, Embase, Web of Science and PubMed from inception until July 2017, using keywords and subject headings related to pregnancy and ANCA-associated vasculitis. Abstract screening, data extraction and risk of bias assessment using the National Institute of Health tool for case reports and case series were performed in duplicate. Vasculitis- and pregnancy-related outcomes were obtained, and pooled incidence (95% confidence intervals) calculated. RESULTS: After screening 628 titles and 183 full texts, we included nine studies reporting 124 pregnancies in 89 women. Most studies (77.8%) had low risk of bias. There were 119 (91%) live births with a mean gestational age of 38.24 (37.35, 39.14) weeks and birth weight of 3.16 (2.95, 3.38) kg. Vasculitis flares were mostly in the ear, nose and throat 12.7% (4.9, 20.6%) and lungs 8.9% (2.9, 14.8%). Most patients were on glucocorticoids (43.5%) and/or Azathioprine (24.2%), increased doses of which successfully treated flares. Adverse outcomes included prematurity 10.3% (3.5, 17.1%), preeclampsia 5.5% (1.4, 9.5%), fetal growth restriction 4.9% (0.9, 8.9%) and congenital anomalies 3.7% (0.3, 7%). CONCLUSION: Women with ANCA-associated vasculitis can be assured that despite large numbers of flares during pregnancy, severe flares are rare and easily managed by high-dose glucocorticoids and immunosuppressants. Pregnancy outcomes are comparable to low-risk populations, apart from an increased risk of 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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".