Use of Belimumab throughout 2 Consecutive Pregnancies in a Patient with Systemic Lupus Erythematosus
Bibliographic record
Abstract
To the Editor: Effects of belimumab on pregnant patients with systemic lupus erythematosus (SLE) are unknown. To our knowledge, we described the first case of a patient with active SLE who was treated with belimumab throughout her pregnancy1. The patient became pregnant again and we describe its successful outcome here. Ethical board approval for case reports is not required per institution policy. Informed consent was obtained from the patient. A 41-year-old woman with difficult-to-control SLE (antinuclear antibody–positive, SSA, dsDNA, antigranulocyte antibody–positive, and lupus nephritis) reported to be pregnant again while receiving belimumab. Belimumab was used successfully during her first pregnancy because of contraindications or side effects to azathioprine, mycophenolate, rituximab, and cyclophosphamide (Table 1). Her second conception resulted in a miscarriage, which was because of aneuploidy and was not thought to be because of SLE. Her SLE was in remission for 18 months before her third conception (Table 1). She maintained treatment with belimumab 10 mg/kg, hydroxychloroquine 400 mg, prednisone 5 mg, and low molecular weight heparin. She … Address correspondence to Dr. A. Kumthekar, Oregon Health and Science University, Division of Arthritis and Rheumatic Diseases (Mail code: OP09), 3181 SW Sam Jackson Park Road, Portland, Oregon 97239, USA. E-mail: kumthean{at}ohsu.edu
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".