Complementing What We Know About Systemic Lupus Erythematosus Pregnancy
Bibliographic record
Abstract
Pregnancy in women with systemic lupus erythematosus (SLE) can increase the risk of disease flare, which in turn increases adverse pregnancy outcomes. Maternal adverse pregnancy outcomes include hypertensive disorders, increased cesarean deliveries, and death, whereas fetal adverse outcomes include fetal loss and death, preterm delivery, and intrauterine growth restriction (IUGR).1 With improved prepregnancy counseling and pregnancy management, adverse pregnancy outcomes in SLE have decreased, although rates of preeclampsia and fetal death remain high.2 Nonetheless, predicting those patients who will flare during pregnancy or have an adverse pregnancy outcome remains challenging. Previous studies have identified some patient characteristics and biomarkers that portend worse pregnancy outcomes: these include current or past renal disease, active disease at conception, hypertension, hematologic abnormalities, and antiphospholipid antibodies.1 Pregnancy success is dependent on modifications to the immune system to ensure that the fetus, which is a hemiallograft, can survive. The complement system plays an important role in these modifications, as complement activation needs to be regulated to circumvent adverse pregnancy outcomes. The presence of complement inhibitors at the maternal-fetal interface helps to achieve this.3 When genetic mutations cause these complement regulators to be defective, recurrent pregnancy loss can ensue.4 Dysregulation of complement can also be associated with pregnancy-induced hypertensive disorders such as preeclampsia.5 Pregnancy complications of antiphospholipid syndrome (APS), including fetal loss and preeclampsia, are thought to be mediated by complement activation. … Address correspondence to Dr. B.L. Bermas, Division of Rheumatic Diseases, UTSouthwestern Medical Center, 2001 Inwood Road, Dallas, TX 75390, USA. Email: bonnie.bermas{at}utsouthwestern.edu.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".